Bibliographic record
Abstract
ABSTRACT Capture‐recapture, a method devised for estimating wildlife population sizes using technologies like bird banding, has been repurposed for use with “rare and elusive” human populations. Capture‐recapture is implemented to count “key populations,” groups that constitute a small portion of the general population but are at high risk of HIV infection, including men who have sex with men. Drawing on ethnographic work in Malawi, I excavate mundane and oblique forms of capture (of labor, value, and viral material) and recapture (producing captive experimental populations) through which key populations come into being. Moving beyond the critical register of dehumanization (counting men as if they were animals) illuminates how efforts to count, care for, and keep track of key populations are mediated by relations of capture and elusion that are simultaneously predatory and capacitating, and entanglements that exceed the confines of a peculiar population‐size estimate method. [ quantification , population , capture , global health , biopolitics , data , Malawi , Africa ]
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".