Accounting for Loss in Fish Stocks

A Word on Life as Biological Asset

in Environment and Society
View More View Less
  • 1 Pratt Institute jtelesca@pratt.edu

ABSTRACT

Why have sea creatures plummeted in size and number, if experts have at their disposal sophisticated techniques to count and predict them, whether tuna, cod, dolphin, or whale? This article conducts a literature review centered on a native category that dominates discourse in marine conservation—stock—by emphasizing the word’s double meaning as both asset and population. It illuminates how a word so commonplace enables the distancing metrics of numerical abstractions to be imposed on living beings for the production of biowealth. By tracking the rise of quantitative expertise, the reader comes to know stock as a referent long aligned with the sovereign preoccupation of managing wealth and society, culminating in the mathematical model recruited today as the principal tool of authority among technocratic elites. Under the prevailing conditions of valuation, the object of marine conservation has become not a fish as being but a biological asset as stock.

Introduction: Taking Stock1

In the world of marine conservation, there is no keyword more important than a fish stock. Commonplace, taken-for-granted, repeated without end, this single term expresses the granular work of unbounded human power over life under extractive capitalism. As the cultural theorist Raymond Williams writes, “Some important social and historical processes occur within language, in ways which indicate how integral the problems of meanings and relationships really are” ([1976] 1983: 22; emphasis in original). This article probes what this word can tell us about the prevailing conditions of valuation, and asks: If experts have at their disposal sophisticated techniques to count sea creatures, then why have many fish stocks still crashed?

Of the nearly 60 meanings of stock recorded in the Oxford English Dictionary (OED), this article foregrounds two: stock as asset, resource, or capital equivalent to an investment in a portfolio of bonds, cash, maybe gold; and stock as population, race, or progenitor of a people, reflected in the phrase, “Mary is of good stock.” Although these meanings seem to share no mutual affinity at first glance, this article claims the contrary: it is precisely in their definitions’ overlap that the reader may access on a microscale the violence of a language used in scientific management when appropriated in a field of power, or the way cold, distancing metrics of numerical abstractions get imposed on living beings, for profit. Although this article tracks the rise of quantitative authority emerging in an administrative state positioned to securitize fish as stock—as asset and population—the scope of this analysis stretches well beyond the domain of bureaucratic management, since even well-meaning environmentalists—the ones eager to save fish from overexploitation—widely adopt this language too.

This article takes as its starting point of analysis a native category that dominates discourse in marine conservation, whether policy maker, scientific expert, industrial handler of fish, or marine advocate: the treatment of tuna, cod, dolphin, and whale as quantifiable stock. Such naming practices illuminate a “regime of value” (Appadurai 1986) that privileges animals as abstract, commodified objects rather than as individual subjects entitled to partake in the survival of their species. To awaken oneself to the way in which language is routinely used and conventionalized for the achievement of this outcome, two literatures must come to cross. Their review is meant not to synthesize multiple patterns of thought. Instead, I survey the need to reevaluate the phenomenon of statistical authority and the material effects it has on entire life-forms, from the perspective of extinction. I aim to reconstruct the historical developments that on the quiet have monetized fish within the very institutions intended to protect them but have not. My main interlocutors are Mary Poovey (1998), Theodore Porter (1995), and James Scott (1998), whose master texts are evaluated in a new light even though none of them shares my own concern with the particular language of a fish stock.

The first literature addresses the history of quantification as it bears on an emerging administrative state beginning in fifteenth-century Europe. Quantification was the means by which a small class of experts in due course came to assert authority over natural and social worlds by managing wealth and society, or assets and populations. An important undercurrent of this literature is the preoccupation with channeling uncertainty through the machinery of risk measured in statistics, probabilities, models, and metrics. The second literature addresses why policy makers recruit scientific management to assert their authority today. It focuses on the mathematical specialization of population dynamics in fisheries science, which underpins and consumes a large share of both the intellectual interest and the explanatory rhetoric of fisheries’ technical elites. The emergence of fish as a biological population—as a class of being derived from the abstraction of race—is important to this discussion.

By highlighting the points of affinity in these literatures, this article makes the following argument: over time, fisheries’ experts have come to employ—as their foremost tool of authority—statistical measures that model the radical uncertainty of future biological assets, rendering the object of marine conservation not the fish as being but as stock. Through the verbiage of a fish stock, the reader comes to know how the life of a sea creature is refigured as “biowealth” (Franklin 2006)—at the joint of both asset and population—which enables extractive capitalism to keep the productive power of nature off the balance sheet (Moore 2015). An important corollary to this argument is that acquiescence to the exterminatory logic of a fish stock depends on an alienated citizenry’s implicit internalization of value commoditized in the extreme, so the flesh of fish and the ocean that gives it life have become just another item for sale for the vast majority of people (see Sullivan 2017).

In support of recent scholarship concerning ecological stocks (Bavington 2010; Höhler and Ziegler 2010), this article offers a genealogy of an axiomatic, stubborn referent that owes its development to extractive capitalism. By tracing the microdynamics of a word over time, the reader may discern the unfolding of loss in marine life over the past centuries (Bolster 2012; Roberts 2007), culminating in an exterminatory scale and rate by the twentieth and twenty-first centuries (Bavington 2010; Telesca 2015). I link the craft dimension of ordinary accounting practices—double-entry bookkeeping, mathematical modeling—with the dominant paradigm of “technoscience” (Latour 1988). As the quantitative analysts became the most trusted authority for husbanding commercial fish, so too did sea creatures transform into commodified, calculable populations that helped “make legible” not only the administrative state on a domestic scale (Scott 1998) but also today’s supranational regulatory regime (see Davis et al. 2012; Telesca 2015). The universal adoption of this discourse—in the press, in policy circles, in marine advocacy campaigns—suggests that even the most conservation-minded may not realize all that the phrase fish stock commits them to (Williams 1977: 69).

Stock Options: On Assets and Populations

Before highlighting core insights from the literatures on quantification in statecraft and on population dynamics in fish, a few words about what is meant by stock as asset and population are warranted. Although this article considers these trajectories separately for analytical purposes, this argument hinges on the recognition of their plasticity and mutual reinforcement. Together their meaning signals a major transformation on this planet because they expose the rationale by which many “wild” fish have gone, despite recent efforts to domesticate them in aquaculture.

As early as 1350 the Stocks or Stocks Market was the name given to an arcade for fish, meat, and other traded goods “in the City of London, on or near the site of the Mansion House.”2 From their earliest days, fish markets were spatially linked to the growth and development of modern securities. These markets were subject to institutional control as the number of participants grew and business became complex (Michie 1999). The fantasy that fish in open water could be secured as stock at all thus found its roots in capital exchange, long a sovereign concern. Similar to land-based forms of livestock, first documented in 1687 as those “domestic animals kept on a farm for use or profit, [especially] cattle, sheep, and pigs,”3 a stock in fish—a group of creatures in the “wild”—signals an important shift toward capital accumulation. Fish became calculable commodities, their profit measurable and their returns predicable. It was as if roving sea creatures could be penned, enclosed, domesticated, exchanged, counted, and ledged for harvest on an upland pasture, their bloodied carcasses hung limp in the yard. In this formulation, a stock in fish is akin to capital supply, to the inventory of products for profit maximization (Höhler and Ziegler 2010: 420), and therefore critical to a nation-state’s economic growth and to its aspirations for empire through control over commodities at sea.

Even so, a stock in fish is never just economy all the way down. The contemporary meaning of a fish stock was still in embryo until the late nineteenth and early twentieth centuries. Not until 1898, after years of flux in herring, did the German scientist Friedrich Heincke introduce the “stock concept” as a biological referent corresponding to what is “presently called a species, as well as units within a species that are a race, a population or a subpopulation” (Booke 1999: 9; emphasis added). The invention of isolated populations of exploited fish that could be counted, assessed, graphed, tabled, inventoried in a metric, and treated as verifiable fact was the order of the day among a narrow band of experts in the early twentieth century. Consensus on what constituted a stock such as genetics or ecological studies would come later (Gauldie 1991), upsetting prior taxonomies.

A scientific vocabulary for fish as stock presupposed their very measurement. Because exploitation impacted different stocks differently, marine scientists began to gather information on how populations themselves were structured. The genetic scientists Gary Carvalho and Lorenz Hauser write, “The stock concept was linked strongly, at least in theory, with the desire to balance the impacts of harvesting with efforts to ensure continued economic returns” (1994: 327). Similar to the administrative practice of fixing and making permanent surnames for tax collection, the conscription of soldiers, the tracking of property, and so on—a relatively recent historical phenomenon (Scott 1998)—the naming of fish according to stock, while clarifying for scientific experts, made possible the animal’s very commodification. To unify, simplify, standardize, make commensurable, and render abstract a living being—whether slave or nonhuman animal—“required the assistance of masters of reckoning,” or those experts that could smooth over obstacles in transactions as large-scale trading networks expanded and capitalism grew (Porter 1995: 25).

Remarkable, then, that fish worldwide no matter the species have plummeted in size and number since Heincke advanced more than a century ago the “stock concept” as the “solution to the expensive problem of fluctuations in marine fisheries exploited under intensifying industrial relations of production” (Bavington 2010: 511–512). A 2015 report by the World Wide Fund for Nature (WWF) in collaboration with the Zoological Society of London sounds the alarm. Marine species have declined by 49 percent in the four decades between 1970 and 2012. Seventy-five percent of some tuna, mackerel, and bonito are gone from the planet. One in four sharks, rays, and skates are threatened with extinction (WWF 2015). The press office of the United Nations remarks on the slaughter less urgently in its report from the Food and Agricultural Organization from 2016: “About 31.4 percent of the commercial wild fish stocks were overfished in 2013” (UN News Centre 2016). To trace developments in stock, I turn to texts about the history of quantification so that I may illuminate why experts recruit statistical modeling techniques to assert their authority over commercial fish today.

Measuring Stock as Asset via Double-Entry Bookkeeping

Stock 50b. In Bookkeeping by Double Entry, the heading (more fully stock account) … of the ledger account which summarizes the assets and liabilities of the trader, firm, or company to whom the books belong.

—OED

The next two sections of this article call upon the history of quantification to appreciate, first, the mercantilist concern with national wealth as it corresponds with the need for figures about foreign and domestic trade (read: stock as asset). The second explores the sovereign’s preoccupation with fertility, mortality, literacy, and the like registered in numbers about society (read: stock as population) (Woolf 1989: 589). Both borrow their etymologies from the natural world. It is worth differentiating between numbers of “fact” and “probability,” as the philosopher Ian Hacking does ([1975] 2006), understanding the latter as modeled tendencies that forecast what may be, for the policy maker to decide what should be done, a discussion left to the next section. Here, I examine what the literary historian Mary Poovey (1998) calls in her probing text “the modern fact” as building block, as that “tiny particle of information, the capsule, the nugget … something compact, robust, down to earth, neutral, bite-sized, byte-sized, the very opposite of theory, conjecture, hypothesis, generalization” (Hacking [1975] 2006: xxx). To transform fish into stock, as asset and population, this section digs deep in centuries past to find the prototype for contemporary metrics in the elementary practice of double-entry bookkeeping, which made possible the aptitude to know, realize, and “make legible” (Scott 1998) wealth and society for the crafting of the administrative state and today’s supranational regulatory regime.

Following the methods first laid out by the Franciscan friar Luca Pacioli in his manual De computis et scripturis from 1494, schoolmaster Hugh Oldcastle and arithmetic writer John Mellis introduced English readers to the practice of accounting in 1588, writing in their compendium Briefe instr. accompts: “Then for your Creditor goe to the letter S. and there enter stocke as followeth: Stocke is in folio 2.”4 Details about financial transactions in which each entry in a ledger satisfied an equation of equal and opposite corresponding effects—of assets and liabilities balanced in the black or red of equity—demonstrated to the magistrates how the specialized experience of the mercantilist could be elevated to the status of expertise (Poovey 1998). Indeed, such prominent theorists as Joseph Schumpeter, Werner Sombart, and Max Weber have long noted that double-entry bookkeeping is closely linked to the emergence of rational capitalism (Carruthers and Espeland 1991). This practice is foundational to the utilitarian calculus of cost-benefit analysis, so favored by policy makers today.

Double-entry bookkeeping as instrument dates to early mercantile capitalism and “a new theory of government known as reason of state” (Poovey 1998: xvii; emphasis added), a phrase that signals the growth of the administrative state in reaction to the instability that overwhelmed Europe after the Reformation. Although its meaning has since changed, it was used during the fifteenth and sixteenth centuries to designate the secular reasoning for government and the elimination of imperial rule. The “art of governing” found its rationale and domain of application no longer in faith and church but in the state and the ability of its prince to exert sovereignty over other “men.” The philosopher Michel Foucault (2007) illuminates the implications of this change in lectures that advance his concept of “governmentality.” Suffice it to say here that merchants appealed to reason of state theorists because they had the instrument to enhance their prestige: double-entry bookkeeping, which took the unwieldy mass of cluttered prose about trade (Carruthers and Espeland 1991) and simplified it through the abstract, “cool idiom of number” (Appadurai 1993: 323). The preoccupation with metrics presented the magistrate with more than just an administrative headache; it revealed a vital aspect of state security tied to the concern of governing wealth and society (Scott 1998: 29), or assets and populations.

In mercantile capitalism, recall that Europe was without precious metals, which meant that wealth was made in distant lands through international trade. Yet the king could not always name his price, let alone guarantee the value of goods, despite the fact that they were essential to securing his power. Better to consult the merchant who knew commerce in its own terms rather than the “legalists who were interested in defending absolute values,” making “trade a fit instrument for political use” (Poovey 1998: 74). The once lowly merchant thus enhanced his status through the discipline embodied in the double-entry system, just as learned men appropriated his methods to demonstrate their authority to the sovereign. In this account, the merchant balanced the book by obeying principles that could render on this earth the order of God’s harmonious design in nature (Appadurai 2016: 73; Poovey 1998).

In Trust in Numbers, the historian Theodore Porter (1995) grounds his title’s claim in the comingling of academic professionals, disciplinary knowledge, and public officials from the early nineteenth century onward. Poovey reaches further back in time, and shows how “the modern fact” can be traced to the elementary practice of double-entry bookkeeping. Poovey finds that over time numbers attained an aura of authority in the modern era as they circulated in networks of hierarchy and exchange, buoyed by the growing cultural investment in mathematics and the influence of key figures in natural philosophy. In the seventeenth century, Francis Bacon sought to ground knowledge on empirical fact rather than on universal assertions or “metaphysical essences” (Poovey 1998: 29), once the cornerstone of ancient knowledge. Poovey writes: “Far from arguing that the human senses were too frail to produce knowledge about the world, the double-entry system confidently showed how such knowledge could be created” (41). The prominent English merchant and mercantilist thinker Edward Misselden could thus proclaim accounting a “science” in 1623—that is, a way to visualize what had previously been hidden (78). That a “science” of accounting was found through the rudimentary methods of quantification anticipates the modeling techniques to which population dynamics in fisheries is indebted, discussed below.

To discover an unknown nation through the description of its particulars (Woolf 1989: 602) made possible a “science” of wealth and society, which underwrote the monarch’s greatness by giving him a “synoptic view” (Scott 1998: 2) for policy formation. Bacon claimed, “A strong government needed good information” (Poovey 1998: 126). He meant that data about who the king ruled, where he ruled, and what he ruled in accordance with numbers on domestic production, disease, and Bible-reading publics structured his faculty of cultivating strength in nation (Anderson [1983] 1991).

With double-entry bookkeeping, the merchant had the device to manage transactions concerning wealth and society for the sovereign. Required were not collective witnessing or anecdotal narratives but rather the precision of number. It is remarkable that numbers until the late sixteenth century carried a pejorative connotation, once associated with black magic (Poovey 1998). Over time, numbers as a representational type came to signify the impersonal, the impartial, the rational, the objective, a form free of influence, author, interest, language barriers, and the accompanying problems of translation. As a “technology of trust,” numbers base their authority in the anonymous and the institutional rather than in the personal and the divine (Porter 1995: 214). In the newfound rhetoric of the secular, metrics ensured rulers of their dominion and convinced taxpayers to ante up. In this formulation, “trust in numbers” (Porter 1995) is a consequence not of mathematics per se, “but of the drive towards democratic government” (Hacking [1975] 2006: xxxii) by way of its administration. Even so, the tension between parliamentary rule and technocratic authoritarianism remains unresolved (Porter 1995: 146).

In double-entry accounting, to be precise meant the expert had to be consistent, not accurate or exact (Poovey 1998: 78). “Mechanical objectivity,” to use Porter’s phrase, implies then and now that accuracy has no meaning if the procedure cannot be performed elsewhere by strangers in the scientific community (Porter 1995: 27–29; Scott 1998: 81). Similar to the mathematical model, discussed shortly, double-entry bookkeeping prioritizes expertise as orderly, methodical, objective, “grounded in specific techniques sanctioned by a body of specialists,” allowing judgment to seemingly disappear (Porter 1995: 7)—just the facts, as it were. The actual writer of mathematical fact became invisible, nameless, effaced (Poovey 1998: 116–117), since objectivity implied not truth but impersonality (Porter 1995: 74). The apparent objectivity of mathematics worked to counteract the charge of corruption by moneyed interests (Poovey 1998: 120).

Today’s technoscientists assessing fish stocks are heir to the double-entry system of centuries past. Consider how the prestige of number contributed to the explosion in bureaucracy and the state officials that people them, many of whom are involved in some manner with collecting or processing numerical information in records (Poovey 1998: 142). What is now known as statistics or “facts of state” did not emerge as a category of knowledge until the early nineteenth century (Porter 1986), even though numbers long played a central role in preparing public administrators for duty. The historian Stuart Woolf recalls that the emperor of France during the Napoleonic years of the early nineteenth century demanded “instant, absolute and immediately utilizable information. The failure to produce in eight days a complete statistic of manufactures, divided by industry, to include all establishments, each with number of workers, quantity and value of production, led to the Bureau [of Statistic’s] suppression” (1989: 600). Yet the experts who built the regulatory state did not merely ready numbers, or observe and map a reality already born of the heavens. They made the reality they described through metrics—to manage it, to control it, to solve problems associated with it as a population, space, or nonhuman nature under their jurisdiction (Scott 1998: 82). Neat mathematical arrangements eased how the experts engineered control over the wayward, including itinerant sea creatures.

Modeling Stock as Population

Stock 3d. A race, ethnical kindred; also, a race or family (of animals or plants); a related group, “family” (of languages).

—OED

Modeling probabilities—similar in effect to the creation of the rudimentary “modern fact” (Poovey 1998) in double-entry bookkeeping—has become a treasured device for the experts managing fish today. This section foregrounds modeling as a technique that takes nonhuman nature into account by aggregating what statisticians call a “population,” or a conceptual unit that marks a collective by imposing order on heterogeneous material. Consider that this approach aligns with what Raymond Williams calls the making of “a singular and essential nature, with consistent and reconcilable laws,” which writes out an awful lot of history and obscures the assumptions that underpin the dominant interpretations of experience in society (1977: 70–71).

This discussion begins by expanding on a point mentioned earlier about the period of turmoil and disarray to which the reason of state theorists responded in post-Reformation Europe. Here the reader finds an important thread in the literature on bureaucratic power in relation to quantification: the problem of uncertainty, which no ruler, no ruling class, no double-entry book, no model can ever fully eradicate. The ocean as medium plays a decisive role here, since shipwrecks and storms at sea in addition to the wild fluctuations of currency (Poovey 1998: 36) were beyond human control. Budding capitalists were intent to devise ways to mitigate loss in the early days of actuarial science. The British-based Lloyd’s of London insured fleets against unruly seas as early as 1688 from a coffeehouse where sailors made impromptu deals (Casey 2010: 131–132). In fact, the very first security emerged once enterprising merchants pooled their money and expanded their capital by distributing risk in seafaring ventures, issuing stock in the world’s first multinational, the Dutch East India Company, in 1602. Of course, the profit motive was not the only engine that drove capitalist development, as Weber ([1958] 2003) made clear long ago when showing how the Calvinists managed uncertainty in this life for an eternal one. Recent work on the theological and ethical underpinnings of the credit and derivatives markets extends this problematic (Appadurai 2016; Maurer 2002).

To understand the role of uncertainty in the bureaucratic penchant for number, I turn to the economist Frank Knight, who, nearly a century ago, made an important distinction between risk and uncertainty. For Knight, uncertainty signifies unknown outcomes that cannot be quantified and thus cannot be modeled (Appadurai 2016: 44–46; Derman 2011: 155). By contrast, risk implies data that can be quantified for the purpose of modeling futures. The statistical analysts in fish population dynamics measure risk by approximation and ask how many swordfish nation-states caught this year so that the model may forecast how many swordfish they should catch next year. To get the answer, the mathematical experts model the animal’s growth, birth, and mortality rates but cannot account for such uncertainties as ocean acidification and the fluctuation in ocean temperature, which significantly impact the reproductive capacity of fish. Notice the bridge between “is” (what information is known by approximation) and “ought” (what fisheries policy should be) (Hacking 1990). Modeling helps policy makers see a stock not yet visible so that it performs as it should. Emanuel Derman writes that the model “stubbornly assumes that all uncertainties about the future are quantifiable. That’s why it’s a model of a possible world rather than a theory about the one we live in” (2011: 156; emphasis added). As the modelers “make the leap from the space of probability (quantifiable) to the space of uncertainty (unquantifiable)” (Appadurai 2016: 81), what is then possible inside bureaucratic zones opens the door to elaborating and refining probabilistic schemes, distortions, and projections (25) that carry the weight of governmental authority and mathematical expertise, even though policy outcomes may appear quite independent of the actual state of marine life or the oceans’ impaired (re)productive capacities. A simulation that models, say, the stock of Atlantic bluefin tuna can be “mathematically correct and statistically defensible” (Poovey 2015: 223) but far from the reality it forecasts.

The previous section’s concern about “the modern fact” (Poovey 1998) illustrated how experts measured relative frequencies under different conditions and events in the past or present, such as suicide. By contrast, sophisticated mathematical models produce probabilities that express how confident one is in a future that, by definition, is unknown (Hacking [1975] 2006). I leave the intricacies about how models actually work to others (Derman 2011; Edwards 2010), recognizing more generally that models are at once proximate to the phenomenon they seek to simulate, just as they operate at a distance from it (MacKenzie 2006). Similar to the double-entry book, the model as instrument simplifies, abstracts, makes impersonal its author and generates outcomes that are precise rather than accurate. It looks forward, not backward, unable to reconstruct history. Instead, it takes a present situation to predict an outcome based on a “plausible, if oversimplified, scenario” (Kingsland 1985: 5).

Pertinent to this discussion is the “risk society” made famous by the sociologist Ulrich Beck, which signifies, in his words, “an epoch in which the dark sides of progress increasingly come to dominate social debate … In the new ecological conflict … what is at stake are negatives: losses, devastation, threats” (1995: 2–3), hazards, dangers, which dominate the systematic ways in which bureaucracies manage risk (Miller et al. 2008) as a consequence of modernization and anxieties about the future (Beck 1995; Giddens 1990). In this managerial view, risk-as-threat-to-society overwhelms its other important dimension, to extrapolate from the political scientist Emily Nacol (2016)—that is, risk also signals the opportunity for profit. The mercantilist Misselden understood in the early seventeenth century that what drove trade were not assurances from the monarch or parity in exchange, but the potential for “‘profit to him who is willing to risk’” (quoted in Poovey 1998: 74). From this perspective, when considering the trajectories of stock as population and asset, the models used to measure its volatility must be seen, simultaneously, as an effort to manage the threat of lost fish for society and to exploit that risk for wealth.

In sum, experts deploy probabilities as mathematical evidence to control uncertainty through the apparatus of risk when making decisions about the future, all in a bid to perform how rational the enlightened “man” is (Daston 1988) under secular rule. Hacking ([1975] 2006) traces the emergence of probabilistic thinking to the Renaissance, which unleashed what he (1990) calls the “avalanche of printed numbers” by the end of the Napoleonic era in the early nineteenth century. A complementary text by the statistician Stephen Stigler (1986) describes the historical development of statistics across fields from geodesy to psychology in the nineteenth century, specifying how the combination of observations first in astronomy were used to play by analogy the game of chance. Over time, the fear that the error of one measurement would contaminate or multiply others gave way to the theoretical foundation for the laws of regression and correlation and, not least, to statistical inference. By the late nineteenth century, experts aimed “to model the range of the normal … to create the most sophisticated models from available data, often using mathematical formulas” (Poovey 1998: 3; emphasis in original). Yet wild cards in the deck remained, since the uncertainties associated with the unknown, the unforeseen, the uncontrollable were still outside the modeler’s purview (Scott 1998: 344).

The scientists who work with mathematical models in fisheries worry most about stock as population, rather than as asset, at least in principle. To appreciate why population is an important category of technocratic rule, I reference what Foucault called in disparate texts “biopower,” or the forms of power exercised over living beings—as populations—to assure their very management, which developed once the sovereign took hygiene, sexuality, nutrition, and other matters of the body for challenges to his order by the end of the eighteenth century. A biopolitical framework offers this discussion clarity on its logic of technique: intervention operates at the level of the aggregate population—rather than at the level of the individual being (Patton 2016: 105)—since what can be assessed, measured, and rendered predictable for its optimization are detailed, statistical accounts of the whole. For commercial fishers to trade one lone sardine under technocratic rule, its aggregate first had to be invented, homogenized, made uniform, and secured as a commensurable stock, which opens a fruitful line of inquiry into the way biopolitics and economy—specified here as population and asset—are co-constituted (Larsen 2007: 11).

When biopower is leveraged in animal studies (Nealon 2016), the point is not to subtract the human or substitute the animal for the human, as if it were a zero-sum game, but to render the status of populations a constant negotiation among experts preoccupied with the biological in the exercise of power. This mode of governing makes possible the disturbing politics that designate populations by biological substrate—that is, by race. For Foucault, the division of humans by race was “a basic mechanism for modern state power,” since the state promoted some but not all lives in the “old sovereign right to kill” (Patton 2016: 112). Proximate reasoning applies in the world of fisheries, since exploited animals are often apprehended as a self-sustaining group of abstract beings that persist in geographic areas over ecological time (Sinclair and Solemdal 1988).

The German scientist Heincke, credited with transforming fish into stock, developed what he called “‘different races of herrings’” (quoted in Gauldie 1991: 725) with the aim of bringing landings into line through regulation. The environmental geographer Dean Bavington writes, “Heincke borrowed the population concept from human demography to develop a powerful quantitative methodology to distinguish aggregations, or stocks, of wild fish” (2010: 511). Heincke assumed, adds Robert Gauldie, “that if a population is normally distributed, it must be a ‘pure’ race, i.e. genetically homogenous” (1991: 726). In developing a rudimentary kind of multivariate statistics, Heincke calculated the range of the normal by adapting methods from the dark days in anthropology (Bavington 2010: 512–513) when the study of biological characteristics justified the shaming of “undesirables.” Heincke relied on no less of an authority than Karl Pearson, the founder of the discipline of mathematical statistics, the well-known proponent of eugenics, the one who in an influential paper from 1894 argued for “separate races of crabs” based on shell length (Gauldie 1991: 726). Historical research on understanding these entanglements is needed5 and may prove profoundly at odds with the stories told about the birth of fisheries science and its esteemed forebears. Even so, it is clear that numbers are never as value-free as their champions persist in imagining (MacKenzie 1999), even in the pursuit of theoretical rather than applied science.

For a population to be ledged an asset fit for trade, early mathematical statisticians manufactured fish by their aggregate according to the abstraction of race. This approach is similar to the methods of scientific management adopted on the plantation, which “rendered slaves not as individuals but as abstract, commoditized units of labor, many of which could be combined to make a whole” (Rosenthal 2016: 76). To ready a living being for market, given the rise in consumer demand, a stock had to be invented and fish reduced to a singular, abstract class of being, contained, ordered, measured, quantified, and commensurable based on available taxonomies, which statisticians found in the eugenics movement. In an impassioned response to his critics—the ones still tinkering with natural history based on the descriptive nomenclature of Carl Linnaeus—Heincke wrote in 1898, “This aversion toward measurement and numbers, which at times is heightened into contempt, is incomprehensible, inadmissible and unpardonable when the scholar demands that his labours be regarded as a contribution to the knowledge of the true laws of nature” (quoted in Bavington 2010: 513). Here is the subject for the next section.

Quantifying Stock in Population Dynamics

Similar to the emergence of scientific forestry, which James Scott (1998) discusses in his influential book Seeing Like a State, fisheries science over the years devised techniques that enhanced the legibility of nonhuman nature to policy makers by making convenient a standardized commodity for market, done not to describe an ecological system already made but to create it through methods that give the category stock the force of law (see Scott 1998: 3). Although these methods “bore a family resemblance to the schemes of legibility and standardization devised” in earlier centuries, new was the magnitude of the transformation unleashed by the instruments of statecraft (343). The carnage in fish post–World War II happened despite, or because of, the clever new mathematical models charged with managing uncertainty by quantifying risk under the rationale that they maximized profit from commodified living beings, stock by stock.

State bureaucrats first began to quantify fish during the Industrial Revolution of the nineteenth century, when coal-powered steamboats replaced what was once free in wind and tide. The mechanization of fishing—and the standardization it required—meant less fish since more were caught (Cushing 1988: 102). By 1864, the government of Norway recruited marine biologists to discern why populations of cod fluctuated so much. In less than 20 years, Norway established a bureaucratic agency to study the variation in its fisheries, and equipped it with a ship, hatchery, and laboratories. Other industrialized nations followed suit (Smith 1994: 1). Eager to keep pace with research in neighboring Canada about the commercially important inland and coastal species of halibut, salmon, cod, haddock, and mackerel, the United States under its Commissioner of Fisheries, in 1928, called for the development of a “distinct branch of scientific study, which may be termed ‘fishery science’” (unnamed official, quoted in Smith 1994: 3). The “quantifying spirit” of eighteenth-century Europe (Heilbron 1990) came to ground by aligning with an “instrumentalist view of nature, with its emphasis on acquisition” (Porter 1995: 85), a decidedly utilitarian enterprise, as the greatest good for the greatest number.

Today the field of fisheries science includes such disparate specializations as oceanography, biology, ecology, and population dynamics, each with its own questions, concepts, and methodologies. Their agendas differ by country and geography, from freshwater environments to saltwater seas. According to the fisheries scientist Tim D. Smith, in reality, far from the lab and the expeditions at sea, “the term ‘fishery science’ is used as an administrative tool referring to all aspects of the study of commercially valuable marine animals, using whatever scientific methodologies are appropriate” (1994: 4; emphasis added). The project of building the administrative state through the scientific regulation of natural “resource” markets never allowed the specializations focused on commercial fish to mature into fields independent from their political and economic interests (Finley 2011; Hubbard 2006; Smith 1994). Fisheries science gave the impression that the regulatory state did not intend to interfere with private enterprise.

The king of all science in fisheries management today is population dynamics. It is designed to inventory the number of “wild” fish in stock so that managers may determine strategies for profitable exploitation. By the 1920s, organizations such as the International Council for the Exploration of the Sea disavowed approaches that searched for the biological and physical factors shaping an animal’s habitat and reproductive capacity. Population studies that measured plenty in commercial fish became the goal (Hubbard 2006: 11). By the 1930s, the new methods mathematically modeling the dynamics of fish populations had all but replaced natural histories (Bavington 2010; Smith 1994). Many of their advocates remained loyal to the English biologist and Darwinian devotee Thomas Henry Huxley, the towering nineteenth-century figure who argued that overfishing was impossible (Hubbard 2006). This worldview still proves decisive in fisheries management today.

By the middle of the twentieth century, fish population dynamics was at the forefront of “economic biology” with its “use of statistical methods, mathematical modeling and life-table analysis” (Kingsland 1985: 4). For the layperson, population dynamics in fisheries science describes changes in what experts call “abundance”—one stock at a time—or to what degree a fish population grows or shrinks over the years based on birth, growth, and mortality rates. Its models aim to create a plausible, if oversimplified, real-world scenario about the future of a fish stock. The masculinist practice of imposing universal mathematical truths on nonhuman nature implied that this expertise minimized history, since what was favored was not the particular or the erratic but rather the harmonious, unified whole of a population in equilibrium (Kingsland 1985). Uncertain ecological factors such as how one species interacts with another are outside the forecasting models, as are the vicissitudes in regulation and state subsidies, which affect how hard a country fishes. The great simplification of the ocean into a machine with cogs of isolated fish was necessary for population dynamics to become a rigorous technical discipline that could be codified, taught, professionalized. It “severely bracketed, or assumed to be constant” a complex set of relations between species and how humans interacted with them, compartmentalizing poorly understood creatures one by one so that they could be rendered a single element of instrumental value (Scott 1998: 19–22).

As state bureaucrats invested in the promise of population dynamics, other specializations in the scientific management of fish became marginalized. Many fisheries biologists simply did not understand the complexity of the models under review (Smith 1994). I borrow from the writer Douglas Whynott, who nicely summarizes the turf war in fisheries science that I too have observed while studying Atlantic bluefin tuna management firsthand:

To the field biologists who had traditionally spent a few years around the docks before doing their research, the … computer modelers seemed arrogant and stiff. The [biologists] didn’t understand the new systems and didn’t know how to question them, and the [modelers] seemed to have all the answers anyway, since in population dynamics it was necessary to anticipate all the questions, and cover the arguments in the way a trial lawyer prepared for a case. As a result, the whiz kids, the number crunchers, as they were called, tended to advance quickly to positions of leadership.

(1995: 145)
Such fault lines among experts demonstrate that fisheries science—however married to metrics as a way of knowing—must not be treated as a uniform, wholly integrated discipline (Smith 1994).

As the technocrats extended the influence of fish population dynamics, it was in the 1950s that fisheries management as we know it took shape (Bavington 2010: 514). In responding to the mounting pressure on commercial fish, two young researchers, Raymond Beverton and Sidney Holt, published in 1957 their groundbreaking mathematical model following the “great law of fishing” by their mentor, Michael Graham, Britain’s chief fisheries scientist after World War II. It stated: “Fisheries that are unlimited become unprofitable” (Finley 2011: 85). Beverton and Holt’s (1957) approach identified fishing mortality as a single variable, which, if controlled, would generate the highest catch whatever the vagaries of “recruitment,” or those young fish that enter the fishery alongside the already exploited stock of older fish. To appreciate this reductive move, Scott writes: “The clarity of the high-modernist optic is due to its resolute singularity. Its simplifying fiction is that, for any activity or process that comes under its scrutiny, there is only one thing going on” (1998: 347). Radar locked on one species, the Beverton-Holt model does not account for multiple species interacting in the very ecosystem that they share. Similar simplifications were made for the more widely adopted stock assessment model proposed by Milner Baily Schaefer (1954, 1957). The Schaefer model is now at the core of most international fisheries agreements6 in large measure because it speaks the grammar of contemporary fisheries management—“maximum sustainable yield” (MSY)—which assumes a single stock is reasonably stable over time and can be maintained into perpetuity.

Although the concept of “yield” first appeared in texts on forestry mathematics by Johann Ehrenfried Vierenklee in 1767 (Lowood 1990: 338), and although fisheries science has borrowed its quantifying rationale from the land-based technologies of forestry (Hubbard 2006), fish are not wood. The capture of roaming sea creatures requires international law and diplomacy. Shortly after World War II, diplomats from the United States championed the idea of MSY under the impression that mathematical rigor would make the world safe from overfishing (Finley 2011). By 1955, MSY took hold—and never looked back—when nation-states came to Rome for the International Technical Conference on the Conservation of the Living Resources of the Sea (Finley 2011; Smith 1994), which in principle provided “technical” advice on fishing in anticipation of the legally binding instruments related to the Law of the Sea Conventions of 1958. The historian Carmel Finley writes that the policy makers in Rome assumed fish were “resilient, stable populations that had ‘surplus’ individuals that could be safely harvested. There would be little harm if a few too many fish were occasionally taken” (2011: 136). For these experts at midcentury, the sea was “inexhaustible” (Daniel and Minot [1954] 1961).

At the level of policy, not harvesting “surplus” meant fish went to waste. To maximize “yield,” countries—by definition—had to exploit commercial fish to the greatest extent possible. Claims about rights to fish and about rights of access to them assured rich countries that if poor ones could not extract the “surplus,” then, legally, the industrialized nations certainly could. This rationale allowed rich states to grow their economies by extracting fish at rates “near a crisis point” (Hubbard 2006: 189). It also implied “that fishing could not be restricted until there was scientific proof” of stock decline (Finley 2011: 88). Population dynamics thus became a tool for the enclosure of the sea because it placed the burden of proof on the nation-states that had the bureaucratic know-how to frame scientific evidence about a population’s depletion (Finley 2011: 135), without ever accounting for the significant number of dead, discarded fish from the capture of nontarget species. As the political theorist Yaron Ezrahi (1990) argues, the interaction between scientific and political authority is less about the rationalization of politics than it is about the utilitarian use of science as a source of legitimacy when views and interests compete.

Over time, nation-states adopted the concept of MSY in the legally binding Law of the Sea Conventions of 1958, later revised in 1982, in addition to other regulatory agreements. Since then, the penchant for modeling fish stocks has grown. That modeling has intensified as the industry has privatized (Carothers and Chambers 2012: 42) is not coincidental. Modeling is the instrument par excellence capable of rationalizing the inventory of fish as a commodified being, as an abstract, standardized population isolated in its biome, allowing well-financed commercial fishers to better compete under conditions of growing economic concentration.

The less the overseer is trusted to save the fish—since past acts have shown it, over time, to have abetted stock collapse—the greater the appeal to numerical forms of knowledge production to justify the overseer’s survival (Porter 1995). Metrics have become instruments of rule because, in principle, they lower the temperature of debate in marine policy. The managerial discourse of “best practices” implies that mathematical evidence officially sanctioned by scientific protocol is necessary to bolster an opinion, since the divine no longer can. The reason is not only to protect against fraud in science but to also ensure that moneyed interests cannot be distinguished in policy making. However inadequate the data, the methods, the modeled forecasts, Porter suggests at the time of his writing that “all this measuring absorbs about 6 percent of the gross national product of the United States” (1995: 28), a remarkable figure indeed.

Conclusion: Out of Stock

Given the material fact that many fish stocks have crashed, this article used as its organizing device the simplicity of word to distill what scientific management oversees in marine conservation and on what rationale its authority rests. It pieced together by what logic of technique state bureaucrats entrusted to conserve marine life came to manage not the animal as being but the export market trading in commercialized fish, to the point that the goal has become the maximization of profit at the threshold of preventing stock collapse. To foster economic growth by controlling nonhuman nature, experts monetized fish as an asset (read: resource) aggregated by population (read: biomass) so that future returns in uncertain stocks were predicable in the short term. They recruited the impersonal, ahistorical, mathematical model from population dynamics as the most trusted device under secular rule. Evidence of future plenty could be ledged according to the principles that weighed the cost of fishing against the benefits of catch, first expounded centuries before in the double-entry book. By the 1950s, the accelerated extermination of fish was overdetermined. The logic of “maximum sustainable yield” adopted at the Rome conference gave stock the crushing force of law. Nation-states agreed to “sustain” the fish not for their deserved place in the biome, but instead extracted their population as a biological asset to maximize profit using the social authority of number.

To be clear, fisheries science is not the problem. The lesson is not that population dynamics should be dissolved, or acquitted, or that a better model with better data using better computer technology with better defined parameters will solve the overfishing problem. That data can be fine-tuned and made ever more precise is a myth that endures in fisheries science today (Finley 2011: 162). This rationale corresponds to the assumption that overfishing is only a technical problem that the state can solve by deciding when and how forcefully to regulate based on the mathematical evidence of neutral experts in fisheries science.

The rendering of fish into stock must be seen as part of the state’s ongoing project of simplification, riddled as it is “with inaccuracies, omissions, faulty aggregations, fraud, negligence, political distortion and so on” (Scott 1998: 80). Although “state simplifications” can never fully realize the abstractions on which they rest, technocrats continue to invest in them anyway, seduced as they and citizens are by quantification (Merry 2016) and the evidentiary protocols of numbers. No longer rooting their authority in the divine, policy makers must show that their rule is not accidental, arbitrary, or capricious in the secular age. 

To oil the engine of extractive capitalism, the experts who administer wealth for society must continue the emotional labor required of abstracting oceanic life-forms as a form of bureaucratic life (see Helmreich 2009). So violent are its effects that the planet now faces “losing marine species and entire marine ecosystems, such as coral reefs, within a single generation,” says a 2015 report by the International Programme for the State of the Ocean (IPSO). It suggests that “high-intensity stressors” have been “a pre-requisite for all the five global extinction events of the past 600 million years” (Rogers 2013). According to a 2016 study in the journal Nature, the great driver of biodiversity loss is not climate change or rising seas but agriculture (including aquaculture) and overexploitation, or the “harvesting” of species in the “wild” at rates that cannot sustain their regrowth (Maxwell et al. 2016). Seen in this light, we may ask: To what extent have fisheries managers—armed with technologies of measure—contributed to or produced extinction, even if unknowingly? Does the treatment of fish as quantifiable stock underlie and register a cannibalizing logic that, unexamined, assists in managing the animal’s demise? To what extent does the concept of stock expose the rationale by which many fish are gone, if the experts tasked to be their stewards primarily value them as fungible biological assets, rather than as, say, a mystery to be contemplated or a joy to be praised?

The effort to classify, rank, order, and contain living beings on the rungs of social hierarchy—which fisheries scientists accomplished by reducing the animal to stock as an abstract class of being—represents a mode of relating to others seen in domains well beyond marine conservation. This rationale animated the fever to define the animal “kingdom” in its taxonomical splendor (Ritvo 1997), and the geographers’ effort to name and categorize the continents with Europe at their center (Lewis and Wigan 1997). The administrative state took classification to another level when its functionaries quantified nonhuman animals according to the cold metrics of number, making the administrator “of necessity, at least one step—and often several steps—removed” from the populations they governed (Scott 1998: 76). To renounce self-interest in knowledge production, to detach the author from the individual maker of metrics, were conditions that made possible trust in numbers and in policy by extension (Porter 1995: 85–86). An affect of disconnect, of disavowal, of distancing is a requirement of quantitative labor, which poses questions more generally about the role of emotion in technocratic life (Bear 2015; Bear and Mathur 2015).

Etymologies are more than mere curiosities. This analysis is an attempt to get the word out about the need for a critical vocabulary that challenges not only supposed truths and logics of being, but also their components—not only grandiose vocabularies like the “Anthropocene” (Haraway 2015; Moore 2015) and the “tragedy of the commons,” but also their parts. It invites the reader to reflect on the possible harms embedded in the verbiage structuring these totalizing ideas, even if using them for what are critical and reformist—even transformative—purposes. The stakes could not be greater as the sixth mass extinction looms over the human imagination (Kolbert 2014). Not since the age of the dinosaurs has the planet experienced such loss in life.

ACKNOWLEDGMENTS

My sincere thanks to Priya Chandrasekaran, Peter Dimock, Macarena Gomez-Barris, Daniel Pauly, Louis Römer, and anonymous reviewers for their generous commentary. Pratt librarian Nick Dease was a lifesaver.

NOTES

1

To problematize the treatment of fish as stock, this article draws attention to the way language obscures and normalizes dominate values by italicizing the word throughout.

2

Oxford English Dictionary Online, s.v. “stock,” last modified June 2016, http://ezproxy.library.nyu.edu:2639/view/Entry/190595?rskey=MNtbfo&result=1&isAdvanced=false.

3

Oxford English Dictionary Online, “livestock,” last modified June 2016, http://ezproxy.library.nyu.edu:2639/view/Entry/109349?redirectedFrom=livestock.

4

Oxford English Dictionary Online, s.v. “stock,” last modified June 2016, http://ezproxy.library.nyu.edu:2639/view/Entry/190595?rskey=MNtbfo&result=1&isAdvanced=false.

5

See work published in German by the historian Sarah Jansen. My thanks to Peder Anker for this reference.

6

My thanks to Daniel Pauly for clarifying the models used in fish population dynamics.

REFERENCES

  • Anderson, Benedict. 1983 (1991). Imagined Communities: Reflections on the Origin and Spread of Nationalism. New York: Verso.

  • Appadurai, Arjun. 1986. “Introduction: Commodities and the Politics of Value.” In The Social Life of Things: Commodities in Cultural Perspective, ed. Arjun Appadurai, 363. New York: Cambridge University Press.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Appadurai, Arjun. 1993. “Number in the Colonial Imagination.” In Orientalism and the Postcolonial Predicament: Perspectives on South Asia, ed. Carol A. Breckenridge and Peter van der Veer, 314339. Philadelphia: University of Pennsylvania Press.

    • Search Google Scholar
    • Export Citation
  • Appadurai, Arjun. 2016. Banking on Words: The Failure of Language in the Age of Derivative Finance. Chicago: The University of Chicago Press.

    • Search Google Scholar
    • Export Citation
  • Bavington, Dean. 2010. “From Hunting Fish to Managing Populations: Fisheries Science and the Destruction of Newfoundland Cod Fisheries.” Science as Culture 19 (4): 509528.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Bear, Laura. 2015. Navigating Austerity: Currents of Debt along a South Asian River. Stanford, CA: Stanford University Press.

  • Bear, Laura, and Nayanika Mathur. 2015. “Remaking the Public Good: A New Anthropology of Bureaucracy.” The Cambridge Journal of Anthropology 33 (1): 1834.

    • Search Google Scholar
    • Export Citation
  • Beck, Ulrich. 1995. Ecological Enlightenment: Essays on the Politics of the Risk Society. Atlantic Highlands, NJ: Humanities Press.

  • Beverton, Ray J., and Sidney J. Holt. 1957. On the Dynamics of Exploited Fish Populations. Fishery Investigations Series II, vol. 19. London: Ministry of Agriculture, Fisheries and Food.

    • Search Google Scholar
    • Export Citation
  • Bolster, W. Jeffrey. 2012. The Mortal Sea: Fishing the Atlantic in the Age of Sail. Cambridge, MA: Harvard University Press.

  • Booke, Henry E. 1999. “The Stock Concept Revisited: Perspectives on its History in Fisheries.” Fisheries Research 43 (1–3): 911.

  • Carothers, Courtney, and Catherine Chambers. 2012. “Fisheries Privatization and the Remaking of Fishery Systems.” Environment and Society: Advances in Research 3: 3959.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Carruthers, Bruce G., and Wendy Nelson Espeland. 1991. “Accounting for Rationality: Double-Entry Bookkeeping and the Rhetoric of Economic Rationality.” American Journal of Sociology 97 (1): 3169.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Carvalho, Gary, and Lorenz Hauser. 1994. “Molecular Genetics and the Stock Concept in Fisheries.” Reviews in Fish Biology and Fisheries 4 (3): 326350.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Casey, Susan. 2010. The Wave: In Pursuit of the Rogues, Freaks and Giants of the Ocean. New York: Anchor Books.

  • Cushing, David H. 1988. The Provident Sea. New York: Cambridge University Press.

  • Daniel, Hawthorne, and Francis Minot. 1954 (1961). The Inexhaustible Sea. New York: Collier Books.

  • Daston, Lorraine. 1988. Classical Probability in the Enlightenment. Princeton, NJ: Princeton University Press.

  • Davis, Kevin, Angelina Fisher, Benedict Kingsbury, and Sally Engle Merry. 2012. Governance by Indicators: Global Power through Quantification and Rankings. Oxford: Oxford University Press.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Derman, Emanuel. 2011. Models. Behaving. Badly. Why Confusing Illusion with Reality Can Lead to Disaster, on Wall Street and in Life. New York: Free Press.

    • Search Google Scholar
    • Export Citation
  • Edwards, Paul N. 2010. A Vast Machine: Computer Models, Climate Data and the Politics of Global Warming. Cambridge, MA: MIT Press.

  • Ezrahi, Yaron. 1990. The Descent of Icarus: Science and the Transformation of Contemporary Democracy. Cambridge, MA: Harvard University Press.

    • Search Google Scholar
    • Export Citation
  • Finley, Carmel. 2011. All the Fish in the Sea: Maximum Sustainable Yield and the Failure of Fisheries Management. Chicago: The University of Chicago Press.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Foucault, Michel. 2007. Security, Territory, Population: Lectures at the Collège of France, 1977–78. Trans. Graham Burchell. New York: Palgrave Macmillan.

    • Search Google Scholar
    • Export Citation
  • Franklin, Sarah. 2006. “Bio-economies: Biowealth from the Inside Out.” Development 49 (4): 97101.

  • Gauldie, Robert W. 1991. “Taking Stock of Genetic Concepts in Fisheries Management.” Canadian Journal of Fisheries and Aquatic Sciences 48 (4): 722731.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Giddens, Anthony. 1990. Consequences of Modernity. Cambridge, MA: Polity Press.

  • Hacking, Ian. 1975 (2006). The Emergence of Probability: A Philosophical Study of Early Ideas about Probability, Induction and Statistical Inference. 2nd ed. New York: Cambridge University Press.

    • Search Google Scholar
    • Export Citation
  • Hacking, Ian. 1990. The Taming of Chance. New York: Cambridge University Press.

  • Haraway, Donna. 2015. “Anthropocene, Capitalocene, Plantationocene, Chthulucene: Making Kin.” Environmental Humanities 6: 159165.

  • Heilbron, J. L. 1990. “Introductory Essay.” In The Quantifying Spirit in the 18th Century, ed. Tore Frängsmyr, J. L. Heilbron, and Robin E. Rider, 124. Berkeley: University of California Press.

    • Search Google Scholar
    • Export Citation
  • Helmreich, Stefan. 2009. Alien Ocean: Anthropological Voyages in Microbial Seas. Berkeley: University of California Press.

  • Höhler, Sabine, and Rafael Ziegler. 2010. “Nature’s Accountability: Stocks and Stories.” Science as Culture 19 (4): 417430.

  • Hubbard, Jennifer M. 2006. A Science on the Scales: The Rise of Canadian Atlantic Fisheries Biology, 1898–1939. Toronto: University of Toronto Press.

    • Search Google Scholar
    • Export Citation
  • Kingsland, Sharon E. 1985. Modeling Nature: Episodes in the History of Population Ecology. Chicago: The University of Chicago Press.

  • Kolbert, Elizabeth. 2014. The Sixth Extinction: An Unnatural History. New York: Henry Holt & Co.

  • Latour, Bruno. 1988. Science in Action: How to Follow Scientists and Engineers through Society. Cambridge, MA: Harvard University Press.

    • Search Google Scholar
    • Export Citation
  • Larsen, Lars Thorup. 2007. “Speaking Truth to Biopower.” Distinktion: Journal of Social Theory 8 (1): 924.

  • Lewis, Martin W., and Kären Wigen. 1997. The Myth of Continents: A Critique of Metageography. Berkeley: University of California Press

  • Lowood, Henry E. 1990. “The Calculating Forester: Quantification, Cameral Science and the Emergence of Scientific Forestry Management in Germany.” In The Quantifying Spirit in the 18th Century, ed. Tore Frängsmyr, J. L. Heilbron, and Robin E. Rider, 315342. Berkeley: University of California Press.

    • Search Google Scholar
    • Export Citation
  • MacKenzie, Donald. 1999. “Eugenics and the Rise of Mathematical Statistics in Britain.” In Statistics in Society: The Arithmetic of Politics, ed. Daniel Dorling and Stephen Simpson, 5561. New York: Oxford University Press.

    • Search Google Scholar
    • Export Citation
  • MacKenzie, Donald. 2006. An Engine Not a Camera: How Financial Models Shape Markets. Cambridge, MA: MIT Press.

  • Maurer, Bill. 2002. “Repressed Futures: Financial Derivatives’ Theological Unconscious.” Economy and Society 31 (1): 1536.

  • Maxwell, Sean L., Richard A Fuller, Thomas M. Brooks, and James E. M. Watson. 2016. “Biodiversity: The Ravages of Guns, Nets and Bulldozers.” Nature 536 (7615): 143145. http://www.nature.com/news/biodiversity-the-ravages-of-guns-nets-and-bulldozers-1.20381.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Merry, Sally Engle. 2016. The Seductions of Quantification: Measuring Human Rights, Gender Violence and Sex Trafficking. Chicago: The University of Chicago Press.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Michie, Ranald C. 1999. The London Stock Exchange: A History. New York: Oxford University Press.

  • Miller, Peter, Liisa Kurunmäki, and Ted O’Leary. 2008. “Accounting, Hybrids and the Management of Risk.” Accounting, Organizations and Society 33 (7–8): 942967.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Moore, Jason W. 2015. Capitalism in the Web of Life: Ecology and the Accumulation of Capital. New York: Verso.

  • Nacol, Emily. 2016. An Age of Risk: Politics and Economy in Early Modern Britain. Princeton, NJ: Princeton University Press.

  • Nealon, Jeffrey T. 2016. “The Archeology of Biopower: From Plant to Animal Life in The Order of Things.” In Biopower: Foucault and Beyond, ed. Vernon W. Cisney and Nicolae Morar, 138157. Chicago: The University of Chicago Press.

    • Search Google Scholar
    • Export Citation
  • Patton, Paul. 2016. “Power and Biopower in Foucault.” In Biopower: Foucault and Beyond, ed. Vernon W. Cisney and Nicolae Morar, 102117. Chicago: The University of Chicago Press.

    • Search Google Scholar
    • Export Citation
  • Poovey, Mary. 1998. A History of the Modern Fact: Problems of Knowledge in the Sciences of Wealth and Society. Chicago: The University of Chicago Press.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Poovey, Mary. 2015. “On ‘The Limits to Financialization.’Dialogues in Human Geography 5 (2): 220224.

  • Porter, Theodore M. 1986. The Rise of Statistical Thinking, 1820–1900. Princeton, NJ: Princeton University Press.

  • Porter, Theodore M. 1995. Trust in Numbers: The Pursuit of Objectivity in Science and Public Life. Princeton, NJ: Princeton University Press.

    • Search Google Scholar
    • Export Citation
  • Ritvo, Harriet. 1997. The Platypus and the Mermaid and Other Figments of the Classifying Imagination. Cambridge, MA: Harvard University Press.

    • Search Google Scholar
    • Export Citation
  • Roberts, Callum M. 2007. The Unnatural History of the Sea. Washington, DC: Island Press.

  • Rogers, Alex D., ed. 2013. The State of the Ocean Report 2013. West Sussex: International Programme on the State of the Ocean. http://www.stateoftheocean.org/science/state-of-the-ocean-report.

    • Search Google Scholar
    • Export Citation
  • Rosenthal, Caitlin. 2016. “Slavery’s Scientific Management: Masters and Managers.” In Slavery’s Capitalism, ed. Sven Beckert and Seth Rockman, 6286. Philadelphia: University of Pennsylvania Press.

    • Search Google Scholar
    • Export Citation
  • Schaefer, Milner B. 1954. “Some Aspects of the Dynamics of Populations Important to the Management of the Commercial Marine Fisheries.” Bulletin of the Inter-American Tropical Tuna Commission 1 (2): 2756.

    • Search Google Scholar
    • Export Citation
  • Schaefer, Milner B. 1957. “A Study of the Dynamics of Populations of the Fishery for Yellowfin Tuna in the Eastern Tropical Pacific Ocean.” Bulletin of the Inter-American Tropical Tuna Commission 2 (6): 227268.

    • Search Google Scholar
    • Export Citation
  • Scott, James C. 1998. Seeing Like a State: How Certain Schemes to Improve the Human Condition Have Failed. New Haven, CT: Yale University Press.

    • Search Google Scholar
    • Export Citation
  • Sinclair, Michael, and Per Solemdal. 1988. “The Development of ‘Population Thinking’ in Fisheries Biology between 1878 and 1930.” Aquatic Living Resources 1 (3): 189213.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Smith, Tim D. 1994. Scaling Fisheries: The Science of Measuring the Effects of Fishing, 1855–1995. New York: Cambridge University Press.

  • Stigler, Stephen M. 1986. The History of Statistics: The Measurement of Uncertainty before 1900. Cambridge, MA: Harvard University Press.

    • Search Google Scholar
    • Export Citation
  • Sullivan, Sian. 2017. “On ‘Natural Capital,’ ‘Fairy Tales’ and Ideology.” Development and Change 48 (2): 397423.

  • Telesca, Jennifer E. 2015. “Consensus for Whom? Gaming the Market for Atlantic Bluefin Tuna through the Empire of Bureaucracy.” The Cambridge Journal of Anthropology 33 (1): 4964.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • UN News Centre. 2016. “As Global Per-Capita Fish Consumption Hits All-Time High, UN Warns on Overharvesting.” United Nations, 7 July. http://www.un.org/apps/news/story.asp?NewsID=54410#.V4FcKuYrJmA.

    • Search Google Scholar
    • Export Citation
  • Weber, Max. 1958 (2003). The Protestant Ethic and the Spirit of Capitalism. Trans. Talcott Parsons. Mineola, NY: Dover Publications.

  • Whynott, Douglas. 1995. Giant Bluefin. New York: Farrar, Straus and Giroux.

  • Woolf, Stuart. 1989. “Statistics and the Modern State.” Comparative Studies in Society and History 31 (3): 588604.

  • Williams, Raymond. 1976 (1983). Keywords: A Vocabulary of Culture and Society. New York: Oxford University Press.

  • Williams, Raymond. 1977. Problems in Materialism and Culture: Selected Essays. New York: Verso.

  • WWF (World Wide Fund for Nature). 2015. Living Blue Planet Report 2015. Gland, Switzerland: WWF International. http://www.worldwildlife.org/publications/living-blue-planet-report-2015.

    • Search Google Scholar
    • Export Citation

Contributor Notes

JENNIFER E. TELESCA is Assistant Professor of Environmental Justice at Pratt Institute. Her research takes an interdisciplinary approach to ocean studies, spanning the interests of political ecology, science and technology, the human-animal relationship, and environmental diplomacy. Her forthcoming book ethnographically details how a supranational regime regulates marine conservation to the tenor of trade, told through the measured slaughter of Atlantic bluefin tuna, once giant. She has authored work on this subject for The Cambridge Journal of Anthropology, and, on “visual citizenship,” for Humanity. E-mail: jtelesca@pratt.edu

Environment and Society

Advances in Research

  • Anderson, Benedict. 1983 (1991). Imagined Communities: Reflections on the Origin and Spread of Nationalism. New York: Verso.

  • Appadurai, Arjun. 1986. “Introduction: Commodities and the Politics of Value.” In The Social Life of Things: Commodities in Cultural Perspective, ed. Arjun Appadurai, 363. New York: Cambridge University Press.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Appadurai, Arjun. 1993. “Number in the Colonial Imagination.” In Orientalism and the Postcolonial Predicament: Perspectives on South Asia, ed. Carol A. Breckenridge and Peter van der Veer, 314339. Philadelphia: University of Pennsylvania Press.

    • Search Google Scholar
    • Export Citation
  • Appadurai, Arjun. 2016. Banking on Words: The Failure of Language in the Age of Derivative Finance. Chicago: The University of Chicago Press.

    • Search Google Scholar
    • Export Citation
  • Bavington, Dean. 2010. “From Hunting Fish to Managing Populations: Fisheries Science and the Destruction of Newfoundland Cod Fisheries.” Science as Culture 19 (4): 509528.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Bear, Laura. 2015. Navigating Austerity: Currents of Debt along a South Asian River. Stanford, CA: Stanford University Press.

  • Bear, Laura, and Nayanika Mathur. 2015. “Remaking the Public Good: A New Anthropology of Bureaucracy.” The Cambridge Journal of Anthropology 33 (1): 1834.

    • Search Google Scholar
    • Export Citation
  • Beck, Ulrich. 1995. Ecological Enlightenment: Essays on the Politics of the Risk Society. Atlantic Highlands, NJ: Humanities Press.

  • Beverton, Ray J., and Sidney J. Holt. 1957. On the Dynamics of Exploited Fish Populations. Fishery Investigations Series II, vol. 19. London: Ministry of Agriculture, Fisheries and Food.

    • Search Google Scholar
    • Export Citation
  • Bolster, W. Jeffrey. 2012. The Mortal Sea: Fishing the Atlantic in the Age of Sail. Cambridge, MA: Harvard University Press.

  • Booke, Henry E. 1999. “The Stock Concept Revisited: Perspectives on its History in Fisheries.” Fisheries Research 43 (1–3): 911.

  • Carothers, Courtney, and Catherine Chambers. 2012. “Fisheries Privatization and the Remaking of Fishery Systems.” Environment and Society: Advances in Research 3: 3959.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Carruthers, Bruce G., and Wendy Nelson Espeland. 1991. “Accounting for Rationality: Double-Entry Bookkeeping and the Rhetoric of Economic Rationality.” American Journal of Sociology 97 (1): 3169.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Carvalho, Gary, and Lorenz Hauser. 1994. “Molecular Genetics and the Stock Concept in Fisheries.” Reviews in Fish Biology and Fisheries 4 (3): 326350.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Casey, Susan. 2010. The Wave: In Pursuit of the Rogues, Freaks and Giants of the Ocean. New York: Anchor Books.

  • Cushing, David H. 1988. The Provident Sea. New York: Cambridge University Press.

  • Daniel, Hawthorne, and Francis Minot. 1954 (1961). The Inexhaustible Sea. New York: Collier Books.

  • Daston, Lorraine. 1988. Classical Probability in the Enlightenment. Princeton, NJ: Princeton University Press.

  • Davis, Kevin, Angelina Fisher, Benedict Kingsbury, and Sally Engle Merry. 2012. Governance by Indicators: Global Power through Quantification and Rankings. Oxford: Oxford University Press.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Derman, Emanuel. 2011. Models. Behaving. Badly. Why Confusing Illusion with Reality Can Lead to Disaster, on Wall Street and in Life. New York: Free Press.

    • Search Google Scholar
    • Export Citation
  • Edwards, Paul N. 2010. A Vast Machine: Computer Models, Climate Data and the Politics of Global Warming. Cambridge, MA: MIT Press.

  • Ezrahi, Yaron. 1990. The Descent of Icarus: Science and the Transformation of Contemporary Democracy. Cambridge, MA: Harvard University Press.

    • Search Google Scholar
    • Export Citation
  • Finley, Carmel. 2011. All the Fish in the Sea: Maximum Sustainable Yield and the Failure of Fisheries Management. Chicago: The University of Chicago Press.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Foucault, Michel. 2007. Security, Territory, Population: Lectures at the Collège of France, 1977–78. Trans. Graham Burchell. New York: Palgrave Macmillan.

    • Search Google Scholar
    • Export Citation
  • Franklin, Sarah. 2006. “Bio-economies: Biowealth from the Inside Out.” Development 49 (4): 97101.

  • Gauldie, Robert W. 1991. “Taking Stock of Genetic Concepts in Fisheries Management.” Canadian Journal of Fisheries and Aquatic Sciences 48 (4): 722731.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Giddens, Anthony. 1990. Consequences of Modernity. Cambridge, MA: Polity Press.

  • Hacking, Ian. 1975 (2006). The Emergence of Probability: A Philosophical Study of Early Ideas about Probability, Induction and Statistical Inference. 2nd ed. New York: Cambridge University Press.

    • Search Google Scholar
    • Export Citation
  • Hacking, Ian. 1990. The Taming of Chance. New York: Cambridge University Press.

  • Haraway, Donna. 2015. “Anthropocene, Capitalocene, Plantationocene, Chthulucene: Making Kin.” Environmental Humanities 6: 159165.

  • Heilbron, J. L. 1990. “Introductory Essay.” In The Quantifying Spirit in the 18th Century, ed. Tore Frängsmyr, J. L. Heilbron, and Robin E. Rider, 124. Berkeley: University of California Press.

    • Search Google Scholar
    • Export Citation
  • Helmreich, Stefan. 2009. Alien Ocean: Anthropological Voyages in Microbial Seas. Berkeley: University of California Press.

  • Höhler, Sabine, and Rafael Ziegler. 2010. “Nature’s Accountability: Stocks and Stories.” Science as Culture 19 (4): 417430.

  • Hubbard, Jennifer M. 2006. A Science on the Scales: The Rise of Canadian Atlantic Fisheries Biology, 1898–1939. Toronto: University of Toronto Press.

    • Search Google Scholar
    • Export Citation
  • Kingsland, Sharon E. 1985. Modeling Nature: Episodes in the History of Population Ecology. Chicago: The University of Chicago Press.

  • Kolbert, Elizabeth. 2014. The Sixth Extinction: An Unnatural History. New York: Henry Holt & Co.

  • Latour, Bruno. 1988. Science in Action: How to Follow Scientists and Engineers through Society. Cambridge, MA: Harvard University Press.

    • Search Google Scholar
    • Export Citation
  • Larsen, Lars Thorup. 2007. “Speaking Truth to Biopower.” Distinktion: Journal of Social Theory 8 (1): 924.

  • Lewis, Martin W., and Kären Wigen. 1997. The Myth of Continents: A Critique of Metageography. Berkeley: University of California Press

  • Lowood, Henry E. 1990. “The Calculating Forester: Quantification, Cameral Science and the Emergence of Scientific Forestry Management in Germany.” In The Quantifying Spirit in the 18th Century, ed. Tore Frängsmyr, J. L. Heilbron, and Robin E. Rider, 315342. Berkeley: University of California Press.

    • Search Google Scholar
    • Export Citation
  • MacKenzie, Donald. 1999. “Eugenics and the Rise of Mathematical Statistics in Britain.” In Statistics in Society: The Arithmetic of Politics, ed. Daniel Dorling and Stephen Simpson, 5561. New York: Oxford University Press.

    • Search Google Scholar
    • Export Citation
  • MacKenzie, Donald. 2006. An Engine Not a Camera: How Financial Models Shape Markets. Cambridge, MA: MIT Press.

  • Maurer, Bill. 2002. “Repressed Futures: Financial Derivatives’ Theological Unconscious.” Economy and Society 31 (1): 1536.

  • Maxwell, Sean L., Richard A Fuller, Thomas M. Brooks, and James E. M. Watson. 2016. “Biodiversity: The Ravages of Guns, Nets and Bulldozers.” Nature 536 (7615): 143145. http://www.nature.com/news/biodiversity-the-ravages-of-guns-nets-and-bulldozers-1.20381.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Merry, Sally Engle. 2016. The Seductions of Quantification: Measuring Human Rights, Gender Violence and Sex Trafficking. Chicago: The University of Chicago Press.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Michie, Ranald C. 1999. The London Stock Exchange: A History. New York: Oxford University Press.

  • Miller, Peter, Liisa Kurunmäki, and Ted O’Leary. 2008. “Accounting, Hybrids and the Management of Risk.” Accounting, Organizations and Society 33 (7–8): 942967.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Moore, Jason W. 2015. Capitalism in the Web of Life: Ecology and the Accumulation of Capital. New York: Verso.

  • Nacol, Emily. 2016. An Age of Risk: Politics and Economy in Early Modern Britain. Princeton, NJ: Princeton University Press.

  • Nealon, Jeffrey T. 2016. “The Archeology of Biopower: From Plant to Animal Life in The Order of Things.” In Biopower: Foucault and Beyond, ed. Vernon W. Cisney and Nicolae Morar, 138157. Chicago: The University of Chicago Press.

    • Search Google Scholar
    • Export Citation
  • Patton, Paul. 2016. “Power and Biopower in Foucault.” In Biopower: Foucault and Beyond, ed. Vernon W. Cisney and Nicolae Morar, 102117. Chicago: The University of Chicago Press.

    • Search Google Scholar
    • Export Citation
  • Poovey, Mary. 1998. A History of the Modern Fact: Problems of Knowledge in the Sciences of Wealth and Society. Chicago: The University of Chicago Press.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Poovey, Mary. 2015. “On ‘The Limits to Financialization.’Dialogues in Human Geography 5 (2): 220224.

  • Porter, Theodore M. 1986. The Rise of Statistical Thinking, 1820–1900. Princeton, NJ: Princeton University Press.

  • Porter, Theodore M. 1995. Trust in Numbers: The Pursuit of Objectivity in Science and Public Life. Princeton, NJ: Princeton University Press.

    • Search Google Scholar
    • Export Citation
  • Ritvo, Harriet. 1997. The Platypus and the Mermaid and Other Figments of the Classifying Imagination. Cambridge, MA: Harvard University Press.

    • Search Google Scholar
    • Export Citation
  • Roberts, Callum M. 2007. The Unnatural History of the Sea. Washington, DC: Island Press.

  • Rogers, Alex D., ed. 2013. The State of the Ocean Report 2013. West Sussex: International Programme on the State of the Ocean. http://www.stateoftheocean.org/science/state-of-the-ocean-report.

    • Search Google Scholar
    • Export Citation
  • Rosenthal, Caitlin. 2016. “Slavery’s Scientific Management: Masters and Managers.” In Slavery’s Capitalism, ed. Sven Beckert and Seth Rockman, 6286. Philadelphia: University of Pennsylvania Press.

    • Search Google Scholar
    • Export Citation
  • Schaefer, Milner B. 1954. “Some Aspects of the Dynamics of Populations Important to the Management of the Commercial Marine Fisheries.” Bulletin of the Inter-American Tropical Tuna Commission 1 (2): 2756.

    • Search Google Scholar
    • Export Citation
  • Schaefer, Milner B. 1957. “A Study of the Dynamics of Populations of the Fishery for Yellowfin Tuna in the Eastern Tropical Pacific Ocean.” Bulletin of the Inter-American Tropical Tuna Commission 2 (6): 227268.

    • Search Google Scholar
    • Export Citation
  • Scott, James C. 1998. Seeing Like a State: How Certain Schemes to Improve the Human Condition Have Failed. New Haven, CT: Yale University Press.

    • Search Google Scholar
    • Export Citation
  • Sinclair, Michael, and Per Solemdal. 1988. “The Development of ‘Population Thinking’ in Fisheries Biology between 1878 and 1930.” Aquatic Living Resources 1 (3): 189213.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Smith, Tim D. 1994. Scaling Fisheries: The Science of Measuring the Effects of Fishing, 1855–1995. New York: Cambridge University Press.

  • Stigler, Stephen M. 1986. The History of Statistics: The Measurement of Uncertainty before 1900. Cambridge, MA: Harvard University Press.

    • Search Google Scholar
    • Export Citation
  • Sullivan, Sian. 2017. “On ‘Natural Capital,’ ‘Fairy Tales’ and Ideology.” Development and Change 48 (2): 397423.

  • Telesca, Jennifer E. 2015. “Consensus for Whom? Gaming the Market for Atlantic Bluefin Tuna through the Empire of Bureaucracy.” The Cambridge Journal of Anthropology 33 (1): 4964.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • UN News Centre. 2016. “As Global Per-Capita Fish Consumption Hits All-Time High, UN Warns on Overharvesting.” United Nations, 7 July. http://www.un.org/apps/news/story.asp?NewsID=54410#.V4FcKuYrJmA.

    • Search Google Scholar
    • Export Citation
  • Weber, Max. 1958 (2003). The Protestant Ethic and the Spirit of Capitalism. Trans. Talcott Parsons. Mineola, NY: Dover Publications.

  • Whynott, Douglas. 1995. Giant Bluefin. New York: Farrar, Straus and Giroux.

  • Woolf, Stuart. 1989. “Statistics and the Modern State.” Comparative Studies in Society and History 31 (3): 588604.

  • Williams, Raymond. 1976 (1983). Keywords: A Vocabulary of Culture and Society. New York: Oxford University Press.

  • Williams, Raymond. 1977. Problems in Materialism and Culture: Selected Essays. New York: Verso.

  • WWF (World Wide Fund for Nature). 2015. Living Blue Planet Report 2015. Gland, Switzerland: WWF International. http://www.worldwildlife.org/publications/living-blue-planet-report-2015.

    • Search Google Scholar
    • Export Citation

Metrics

All Time Past Year Past 30 Days
Abstract Views 304 190 8
Full Text Views 54 10 4
PDF Downloads 38 19 16