The medical discourse and the sterilization of people with disabilities in the United States, Canada and Colombia: From eugenics to the present
Bibliographic record
Abstract
Science and medicine are not objective or neutral fields of knowledge. Specifically, the medical discourse about people with disabilities has been historically shaped by elements like ideology, and moral, political and economic views. Proof of this, are methods for measuring intelligence, such as Craniometry and IQ testing, and the eugenic scientific theory and movement, which related "feeblemindedness" with gender, racial and social stereotypes, and the degeneration and lack of progress of societies. This work studies current judicial decisions of non-consented sterilization of people with cognitive disabilities of the United States, Canada and Colombia in a comparative perspective, and analyses the different standards and requirements judges have adopted to address this subject. This thesis argues that (1) it is necessary to challenge the way the law tends to base reproductive decisions of people with disabilities mainly on medical expert opinions, relying on these opinions as impartial and objective knowledge; and (2) it is necessary to study the current cases of non-consented sterilization of people with cognitive disabilities in the context of eugenics in each of these countries, where sterilization was used to decide what sorts of people should exist. This work claims that by allowing sterilization decisions to be based on scientific expert opinions, legal systems will forever be immersed in the medical model of disability, where diagnoses are more important than 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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.022 | 0.020 |
| Scholarly communication | 0.007 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".