Samir Shaheen-Hussain, Fighting for a Hand to Hold: Confronting Medical Colonialism against Indigenous Children in Canada
Bibliographic record
Abstract
Fighting for a Hand to Hold is a searing indictment of the Canadian medical establishment’s complicity in the colonial project and the genocide of Indigenous peoples. Samir Shaheen-Hussain, a practicing paediatric doctor who led a successful campaign to force the Quebec government to end its practice of prohibiting adult accompaniment of Indigenous children being airlifted from remote communities for emergency medical care in the cities, places this practice within the context of medical colonialism whereby Indigenous children were forcibly removed from their families by the state for schooling, medical care, foster care, or were the subject of medical experiments as late as the early 1970s. It is an essential story, one that should be compulsory reading in medical schools across Canada and beyond. The book is part of a wider reckoning with the dispossession and genocide of Indigenous peoples in Canada. The 2015 report of the Truth and Reconciliation Commission has forced many settler Canadians to recognise their own complicity in this violence, which reaches into the present. Nationalist narratives are being challenged like never before, with statues being pulled down or defaced and the names of streets or schools being changed. These actions have sparked a strong white nationalist backlash, fed by a right-wing press committed to stoking the culture wars. The very idea of Canada is at stake.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.014 | 0.007 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.014 | 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".