Trading sex for freedom: The influence of Kamala Kempadoo's early scholarship
Bibliographic record
Abstract
Abstract This article revisits Kamala Kempadoo's publications on sex work in the Caribbean in the late 1990s and early 2000s. This period marks both the era before the framework of anti‐trafficking consumed the field of sex work studies and the era when Black feminist ethnographic research on how race, class, and structural violence affected women's lives globally was making an impact across disciplines. By focusing on three key concepts (work, race and gender, and slavery), I show how Kempadoo recontextualized terms used by anti‐prostitution feminists within the history of colonialism and the political economy of 1990s multilateral trade. In so doing, she offered feminist sociologists and anthropologists new possibilities for understanding sexual labor and sexual freedom. The paper then returns to Kempadoo's engagement with the history of enslaved Black women in Caribbean brothels to offer an alternative reading of the relationships between slavery and sex work that now dominate the public relations campaigns of anti‐trafficking organizations. Taken together, Kempadoo's multidisciplinary contributions help feminist anthropology see how important global sex worker activism is to understanding race, gender, and work. This paper further demonstrates how combining critical engagement with feminist ethnographies with historical memory studies can shed new light on contemporary social issues.
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.013 | 0.025 |
| Scholarly communication | 0.012 | 0.008 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.007 | 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".