Bibliographic record
Abstract
Prior to the 1980s, historians of Africa rarely paid explicit attention to sexuality, a topic generally presumed to belong more appropriately to anthropology or psychology. Moreover, when the topic did come up, authors across the disciplines commonly assumed or asserted an African (“native,” customary, tribal, or premodern) sexuality, in the singular and in implicit timelessness. With the advent of HIV/AIDS, however, and following trends in international scholarship that saw a proliferation of close empirical case studies that employed (and extended) Foucauldian and postcolonial theory, this began to change dramatically. Much of the thinking about HIV/AIDS since the 1990s, notably, has stressed the long-term impacts of colonialism, racism, and male migrant labor that underpin the pandemic and that help to explain striking regional disparities. Indeed, the new scholarship argues that ahistorical readings of scientific data, let alone of ethnography or the other social sciences, have actually contributed to the HIV/AIDS crisis by unwittingly promoting colonial-era stereotypes, simplifications, and calumnies against African cultures. Medical anthropologists thus now routinely acknowledge the importance of historical change, context, and specificity to their research on sexual health.1
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.005 | 0.015 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.019 | 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".