Helen Longino, <b><i>Studying Human Behavior: How Scientists Investigate Aggression and Sexuality</i></b>, Chicago: University of Chicago Press, 2013, ISBN 978-0-226-49288-9
Bibliographic record
Abstract
In Studying Human Behavior: How Scientists Investigate Aggression and Sexuality, Helen Longino examines in-depth five approaches to the science of aggression and human sexuality in terms of their epistemological framework, the kinds of knowledge they produce, and their pragmatic goals.Centrally, she tackles the monistic assumption that the knowledge produced by these disciplines can be combined or reduced into to a single, all-encompassing account.Coming from a social epistemological standpoint, Longino argues that this monistic assumption is neither conducive to the goals of behavioral research nor empirically tenable.Her analysis demonstrates how ontological and epistemological factors contribute to the incommensurability of the accounts produced by the approaches.It also shows that the restricted and inadequate dissemination of the products of these approaches has negative social and epistemic consequences.She argues that if we wish to continue to guide our policies with scientific research, we need to embrace a pluralistic stance that accepts the partiality of knowledge that any one approach can generate.Such pluralism ensures the availability of a broader range of research platforms, and also encourages a more cautious approach to how we inform policymakers and publics about scientific research.In this review, we discuss the ways in which this book contributes to Longino's own corpus and to feminist empiricist and philosophical aims more generally.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.007 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.051 | 0.026 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".