Bibliographic record
Abstract
The worldwide AIDS pandemic has brought into focus questions of strong interest to political scientists, such as variations in institutional robustness and in the capacity and willingness of governments to fight the epidemic, as well as the interconnections between local and transnational actors (SwidlerForthcoming). Yet, academic work examining AIDS and politics in Africa remains conspicuously sparse. In The Politics of AIDS in Africa, Amy Patterson aims to fill this gap by tackling two issues: (1) the factors that account for different state responses to the AIDS epidemic, and (2) the role of civil society and international donors in AIDS policymaking. Both issues intersect directly with the traditional concerns of political science because they raise questions about the sources of state capacity, responsiveness, and legitimacy, as well as about the conditions under which positive partnerships between regimes and civil organizations can develop (Boone and Batsell 2001:5–6)....
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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 | 0.007 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.006 | 0.008 |
| Scholarly communication | 0.008 | 0.010 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.006 | 0.004 |
| Insufficient payload (model declined to judge) | 0.007 | 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".