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Record W2745373486 · doi:10.1093/biosci/bix096

Uniting the (Social) Sciences?

2017· article· en· W2745373486 on OpenAlexaff
Louise Barrett

Bibliographic record

VenueBioScience · 2017
Typearticle
Languageen
FieldDecision Sciences
TopicInterdisciplinary Research and Collaboration
Canadian institutionsUniversity of Lethbridge
Fundersnot available
KeywordsSociology

Abstract

fetched live from OpenAlex

Herbert Gintis is the Ernest Hemingway of the behavioral sciences. His sentences are short, sharp, and declarative. There is no preamble. He gets right to it. There is very little in the way of qualification. There is a lot of confident assertion. It can be kind of exhausting. It is also rather exhilarating to read a no-frills, no-nonsense approach to integrating the human behavioral sciences. Gintis is not here to argue and persuade; he is here to tell you how it is. This does not mean that I agree with everything he says, but I like his style: The boldness of the claims made is matched by an amazing breadth of knowledge and a deep appreciation for the contributions made to the social sciences by the likes of Geertz, Mead, Malinowski, Durkheim, Weber, and Bourdieu. His impatience with the lack of a rigorous analytical core in the human behavioral sciences—and his attempt to construct such a framework—does not entail that traditional approaches, such as ethnographic “thick description,” must be done away with altogether. Instead, he actively embraces such descriptive accounts, arguing that these, along with historical insights, are crucial for conceptualizing the dynamic, complex systems of human societies. As a result, statements such as “My suggestion for an analytical core for sociology will be called economic imperialism by some…. My retort is that these fields are in such serious need of a unifying theoretical framework that a little imperialism from more successful fields should be welcome” did not annoy me half as much as they usually do (although let the record show that I still found them deeply annoying). Similarly, when Gintis tackles the contentious, often-vituperative clash over inclusive fitness theory between E. O. Wilson (along with Martin Nowak and Corina Tarnita) and ­virtually everybody else in evolutionary biology, he does so in equally bold fashion, but with a generosity and even-­handedness that gives both sides their due. One obvious reason for this is because Gintis's project is explicitly designed to incorporate a supra-­individual level of analysis into the evolutionary fold. This means embracing multilevel selection, in which group ­selection is conceptualized as selection for groups with a fitness-enhancing size and structure rather than as ­selection among groups, as well as individual-level processes. As he puts it, we need to bring together the “atomistic” inclusive fitness view with the “structural” multilevel- or group-selection view and “analyze the ­corresponding interplay of forces.” In this way, Gintis argues, we can investigate human society more appropriately, avoiding an overly reductionist view that considers everything from an entirely ­individual perspective while also avoiding the idea that, as classic sociological theory would have it, ­individuals are shaped entirely by nebulous, poorly defined “social forces” that press down on them from above. The majority of the work presented in Individuality and Entanglement has been published previously and is brought together here to provide a comprehensive synthesis. On one hand, this has obvious advantages because it allows one to get a good grip on Gintis's body of work, which is impressive in scope. Along the way, one encounters not only the reformulation of economic theory and models, as well as work in gene-culture coevolution (including fascinating work on how selection for the internalization of norms that serve individual interests can enable socially oriented moral norms to “hitchhike” and come along for the ride), but also a historical analysis of the work of sociologist Talcott Parsons and how Gintis builds on this. On the other hand, there is little in the way of linkage between the individual chapters, and it can be hard to get a sense of how these cohere, at least on an initial reading. Happily, Gintis provides a summary of the themes he wishes to develop at the very beginning of the book, and the book is best seen in this light—as the marshaling of evidence in support of a number of interlinked themes rather than a linear argument. Gintis's central theme is that humans are not to be characterized as Homo economicus—selfish maximizers of personal well-being—but as Homo ludens, “man, the game-player”: beings who are fundamentally social and whose rationality can be understood only in a social context—that is, by recognizing society as a game with rules. Everything distinctive about human social life flows from this fact. Playing the game of life, in turn, requires a moral sense: The rules we make are morally binding and not simply instrumental to achieving our goals. Gintis then makes a move that is crucial to his attempt at unifying the social and natural sciences and which represents another important theme: Human minds should be seen as socially entangled. Specifically, human knowledge and beliefs (“cognition”) are argued to be distributed across our social networks and therefore do not reside in individual minds. Here, Gintis echoes anthropologist Edwin Hutchins, who made a very similar argument in his classic Cognition in the Wild, as well as work by Kim Sterelny and Andy Clark on the scaffolded or extended mind, although none of these receive a mention. In Gintis's hands, the idea of entangled minds provides further grounds for criticizing conceptions of humans as rational actors in the Homo economicus mode. Gintis demonstrates that although entangled minds produce behavior that is rational, such behavior does not conform to the standard axioms of rational choice. He then goes on to argue that humans display what he terms “distributed effectivity”—a form of collective rationality that can explain, for example, why we believe it is right to vote for a particular candidate and that our vote has counted for something, even though it is clear that our single vote had no effect on the outcome of the election. This leads to a deeper consideration of human preferences, where Gintis argues that in addition to the self-regarding preferences of standard economics, we have both other-regarding and universal preferences, both of which are crucial to understanding human society (where the latter not only involve consequential moral principles but also include virtues such as courage and loyalty). Importantly, it can be shown that humans trade these preferences against each other and that such trade-offs are explained by rational choice theory, even though the preferences we possess are not those of the classical rational actor. In other words, it is a mistake to view humans as irrational, as many in behavioral economics do, because it is the economists’ rational-actor model that is at fault, not human rationality itself. The final theme that Gintis develops is the case for transdisciplinary research. By Gintis's lights, this means a concerted effort to deal with the fact that psychology, biology, economics, and sociology each make claims that are seen as key to understanding human behavior but which are ignored or denied by all the others. By making each discipline compatible with all the others, as well as compatible with evolutionary theory, it will become possible to unify the behavioral sciences. There is room for everyone in Gintis's account, although, in the main, they have to buck up, knuckle down, and get some mathematics under their belt. I therefore have my doubts that those working in anthropology and sociology will be convinced by Gintis's argument. The telegraphic delivery and unapologetic imperialism play a part in this, but the main reason is that Gintis's analysis ultimately represents a tweaking of established economic (and evolutionary models) that (apparently) assume that our current economic system is somehow inevitable. The inclusion of levels of analysis above the individual and the recognition of a moral component to human behavior are clear advances for those of us in the human evolutionary sciences (although I suspect that there are many in that camp who will object to Gintis's multilevel approach). However, I fear that many in the “traditional” social sciences will see things very differently. As an illustration, reading Gintis reminded me of the oral defense of one my master's students, whose thesis was on maternal investment strategies in vervet monkeys. One member of her committee was a social anthropologist, whose very first comment was, “Why did you choose to adopt a capitalist perspective on this issue?” The incomprehension that produced the question was met with an equal degree of incomprehension on the part of the student. For the latter, it was obvious that economic and biological rationality would coincide in this particular way; for the committee member, it was an untested (and egregious) assumption imposed on the system from the outside, as though capitalism represented the natural order of things. For this reason, I fear that Individuality and Entanglement will succeed only in preaching to the choir. Maybe that is not a bad thing, and maybe that is its intention. Perhaps the aim is simply to enable already-scientific approaches to become more closely aligned and mutually consistent while still leaving space for the more interpretative, hermeneutic approaches. As a pluralist rather than an imperialist, I would like to think so anyway.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.026
metaresearch head score (Gemma)0.034
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Commentary · Consensus signal: Commentary
Teacher disagreement score0.993
Threshold uncertainty score0.136

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0260.034
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0030.005
Science and technology studies0.0070.044
Scholarly communication0.0210.040
Open science0.0030.013
Research integrity0.0100.008
Insufficient payload (model declined to judge)0.0260.005

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.449
GPT teacher head0.575
Teacher spread0.126 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

Study designTheoretical or conceptual
Domainnot available
GenreCommentary

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

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Citations1
Published2017
Admission routes1
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