The forced sterilisation of Indigenous and racialised Peoples: Origins, nature of abuses, impacts and responses
Bibliographic record
Abstract
Purpose This paper aims to draw attention to the global infringement of reproductive rights of Indigenous and racialised Peoples. Design/methodology/approach Narrative literature review. Description and comparative analysis of examples of forced sterilisation. Findings Large-scale sterilisation campaigns were identified in three different regions of the world: North America, Latin America and Europe. Within these, hundreds of thousands of Indigenous and racialised Peoples have been forcibly sterilised as part of state-sponsored procedures, predominantly aimed at women and gestating people. These abuses are continuing in the 21st century and have origins in “racial science” theory. The exact nature of the abuses is identified alongside the long-term health and wellbeing implications. Professional attitudes and behaviours that condoned such practices within healthcare settings are identified. The psychological, social and cultural impact of such practices, including on Indigenous body sovereignty and self-determination, are demonstrated. Practical implications These are twofold: firstly to eradicate any future practice of forced sterilisation and secondly to provide reparations to those affected. Originality/value The analysis brings together scholarship from Indigenous studies alongside that of health and social sciences.
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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 | 0.008 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.011 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| 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".