Intersectionality as method for human rights research: Identifying who is made stateless and how through UN treaty body reviews
Bibliographic record
Abstract
Few theories have generated the kind of international and interdisciplinary engagement as intersectionality. Nevertheless, intersectionality as a research paradigm has yet to gain ground in human rights research. People can experience the same rights violation on multiple grounds, yet human rights research design and methods—like rights frameworks and treaty bodies themselves—tend to examine each form of discrimination separately or additively. This article demonstrates the value of intersectionality as a methodological approach for human rights research by discussing feminist methodological insights developed through a global qualitative study of exclusionary birth registration practices that lead to statelessness. The discussion highlights three intersections that block access to birth certificates: gender, religious, and ethnic discrimination at the civil registrar; disability and ethnic discrimination in contexts of mobility; and discrimination based on gender, race, and migration status in reproductive healthcare. The conclusion offers human rights researchers an intersectional method for analyzing observations from all human rights mechanisms on a particular issue, to gain a more fulsome understanding of the operations of power that violate rights.
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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 | 0.253 | 0.305 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.025 | 0.022 |
| Science and technology studies | 0.012 | 0.037 |
| Scholarly communication | 0.027 | 0.037 |
| Open science | 0.005 | 0.023 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.016 | 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".