Contingency: Interpersonal and Historical Dependencies in HIV Care
Bibliographic record
Abstract
The word ‘uncertainty’ has many relatives, each opening particular analytical possibilities. Within the extended family, we might count: insecurity, indeterminacy, risk, ambiguity, ambivalence, obscurity, opaqueness, invisibility, mystery, confusion, doubtfulness, and scepticism. Some of its cousins seem to admit of positive potential: chance, possibility, subjunctivity, hope. Uncertainty and insecurity are the most prominent members of the family. We can think of uncertainty as a state of mind, and minding, when we are unable to predict the outcome of events or to know with assurance about something that matters to us. Insecurity, the lack of protection from danger, the weakness of arrangements to support us when adversity strikes, gives rise to uncertainty. Dealing with uncertainty is often about trying to make more secure, rather than simply trying to ascertain. And making more secure usually has to do with mobilizing resources in order to exert some degree of control. Both terms are broad and often used rather vaguely, without specifying the focus of uncertainty or the source of insecurity (Whyte 2009). These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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 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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.016 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.009 | 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".