African Diasporic Women’s Narratives: Politics of Resistance, Survival, and Citizenship
Bibliographic record
Abstract
The medical-industrial complex can inflame racialized anxieties about disease control, border control, and access to health care. The Ebola outbreak in 2014 is just one example of how these anxieties intersect on a global scale. This book, which focuses on Saartije Baartman, Audre Lorde, Maryse Condé, Edwidge Danticat, and Grace Nichols, is a timely examination of the “embodied resistance to medical diagnosis” (5). Simone A. James Alexander reminds the reader of the centrality of health to a long history of black feminist intersectional analysis (see also White 1995; Smith 2002). She interrogates “medical profiling” (47) by hetero-patriarchal states and deconstructs the racist stereotypes of black women that undergird such domination. Further, her critique of reactionary U.S. immigration officials who mistreat Haitians entering the United States as potential HIV/AIDS carriers represents one of many occasions for an extended analysis of negotiations and resistances to medical regimes. Refusing to fixate on suffering, Alexander celebrates African Diasporic women’s transgressions of Western femininity, medicine, and citizenship.
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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 | 0.006 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.027 | 0.033 |
| Scholarly communication | 0.008 | 0.009 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.004 | 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".