Collective Biologies: Healing Social Ills through Sexual Health Research in Mexico, Emily A. Wentzell
Bibliographic record
Abstract
To gather the research for Collective Biologies, Emily Wentzell followed middle-class, heterosexual couples in urban Mexico through four years of participation in the Cuernavaca Human Papilloma Virus in Men (HIM) study, a longitudinal, observational medical research study (p. 2). She was interested in how middle-class, un-paid participants incorporate research studies like this into their broader life projects (p. 3). In the book, she analyzes how people’s cultural ideologies regarding health not only influenced their experiences of medical research but “actually enabled them to incorporate research participation into wide-ranging social and biological goals at the levels of the couple, the family, and the Mexican populace” (p. 3). Chapter 1 starts with a narrative of one research project participant, Arturo, linking his experience as a victim of a carjacking to his subsequent religious conversion, changes in his marriage, and finally his participation in the HIM research study five years later. For Arturo, the carjacking led him through a transformation towards the emerging local ideal of emotionally open masculinity. This saw him become a more caring, dedicated, and present spouse and parent, and led to his enrollment in the HIM study as a way for caring for others. This is the way that Wentzell theorizes the lived experience of medical research participants through the analytic of “collective biologies.”
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.014 | 0.002 |
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".