Black subjects in Africa and its diasporas : race and gender in research and writing
Bibliographic record
Abstract
Race, Gender and the Research Subject: An Introduction B.Talton & Q.T.Mills Researching while Black: Interrogating and Navigating Boundaries of Belonging in the Andes S.Busdiecker Posing as Subject: Compromise and the Art of Access in Trinidad H.Neptune Translating Hybrid Cultures: Quandaries of an Indian-Australian Ethnographer in Cuba S.Fernandes Where to Find Black Identity in Buenos Aires J.Anderson 'You Don't Look Groomed': Rethinking Black Barber Shops as Public Spaces in the United States Q.T.Mills The 'Dark Sheep' of the Atlantic World: Following the Transnational Trail of Blacks to Canada D.J.Broyld The Strange Life of Lusotropicalism in Luanda: On Race, Nationality and Sexuality in Angola J.Krug Quenching the Thirst for Data: Beer, Local Connections and Fieldwork in Ghana B.Talton (African-) American Woman Outsider: Nationality, Race and Gender in Field Research in Mozambique F.Henderson Mamatoma 'The Chief's Namesake': Strategies for Research and Belonging in Sierra Leone L.R.Day
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.015 | 0.014 |
| Scholarly communication | 0.010 | 0.007 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| 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".