Intersectionality and the United Nations World Conference Against Racism
Bibliographic record
Abstract
This article analyzes the 2001 World Conference Against Racism (WCAR) held in Durban, South Africa. Utilizing original interviews with civil society delegates in the United States and Canada, along with government documents and media and academic accounts, we challenge prevailing interpretations of the WCAR to show that it was an important space for expressions of an explicit feminist intersectionality approach, especially the intersection of racism with gender. Our findings demonstrate how intersectionality was relevant to the discussions of both state and civil society delegates and served to highlight racialized, gendered, and other discriminatory patterns. Based on this evidence, we argue that the WCAR process played a significant role in advancing a global conversation about intersectionality and therefore carried significant potential for advancing an anti-racist agenda for the twenty-first century. That this is not widely understood or highlighted has to do with challenges to the WCAR, particularly the withdrawal of key states from the process and a negative discourse concerning discussions and scholarly analysis of the WCAR process. We suggest that acknowledging the presence of intersectionality in the WCAR process gestures towards a more accurate historical record. It also suggests both the opportunities and constraints afforded by intersectional analysis in moments of transition and mainstreaming. As such, the “Durban moment,” and the WCAR more broadly, are highly relevant for the study of women, politics, and human rights over the first decade of the twenty-first century.
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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 | 0.011 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.026 | 0.025 |
| Scholarly communication | 0.016 | 0.006 |
| Open science | 0.001 | 0.016 |
| Research integrity | 0.004 | 0.006 |
| 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".