Gender, HIV/AIDS, and Human Security in Africa
Bibliographic record
Abstract
Abstract Using theoretical analysis and empirical findings from case studies in several African countries, the authors of this special issue adopt a feminist analysis of gender relations, HIV/AIDS, and human security in order to expand upon and deepen our understanding of health, development, and security, and how they affect individuals and society. HIV/AIDS can have a destabilizing effect on countries and communities, with consequences for levels of sexual and gender-based violence, poverty, health issues, food insecurity, and broader social, political, and economic challenges. The authors who have contributed to this collection of articles not only challenge us to think more critically and innovatively about the impact of HIV/AIDS as it pertains to gender inequality and human insecurity across Africa, but also they offer fresh insights for rethinking policy and programmatic efforts to address the crisis.
Stored with the screening record, where it is evidence for the labels above.
How this classification was reachedexpand
The three-model screen
all 5,600 screened works →All three models called this out of scope.
Introduction to a special issue on gender, HIV/AIDS, and human security in Africa; the object is health, gender, and security, not research practice.
The work studies gender, HIV/AIDS, and security in Africa rather than research practice.
Special-issue framing of gender, HIV/AIDS, and human security in Africa; domain social science.
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 | 0.002 | 0.002 |
| Science and technology studies | 0.007 | 0.013 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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 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".