Bibliographic record
Abstract
A combination of infl uences over the past few decades has steadily raised the profi le of the anthropology of bioscience and biomedicine, and this fi eld has now become a well-established subdiscipline. 1 This new fi eld has several antecedents.The move within the discipline towards greater refl exivity and the critiques of various kinds of ethnocentrism contributed to the emergence of bioscience as a subject of anthropological study in the 1990s -a transition that was aided by the emergence of the human genome project as a source of both ethical uncertainty and funding for research into its ethical, social, and legal implications (Franklin 1995 ).Anthropological interest in new reproductive technologies in this same period further encouraged a more critical engagement with biological models of "natural facts" (Strathern 1992a(Strathern , 1992b ) ), and Sandra Bamford's ( 2007 ) pioneering account of a society in which physiological explanations of conception play only a minimal role in understandings of both reproduction and kinship has offered a distinctive foil against which to contrast a Euro-American emphasis on biology that has increasingly come to be seen as extreme.At the same time it has remained unclear precisely how biological and physiological explanations function in contemporary society, since they are at once apparently literal and yet are often employed in ways that are self-evidently fi gurative (Franklin 2003 ;Nelkin and Lindee 1995 ).The dual quality of biological explanations also appears as a difference between what people say and what they do (many couples emphasize the importance of having a biological child of their own but will use a variety of means to achieve this end, including other people's eggs and sperm, Thompson 2005 ).Another striking and well-documented distinction is the considerable difference between how Core terms of use, available at https://www.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.007 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".