Unheard Voices: Sisters Share about Institutionalization
Bibliographic record
Abstract
The recent emergence of institutional survivors’ accounts of mistreatment and abuse in Ontario’s institutions for the “feebleminded” offers a window into Canada’s long history of segregation, mistreatment, and neglect of people labelled intellectually disabled. The breaking of this silence has also allowed the stories of others who were deeply affected by institutionalization to come forward. Narratives from siblings of institutionalized individuals, although not first-hand accounts of the life inside institutional walls, offer much needed perspective on the extensive and ongoing effect of institutionalization in the lives of thousands of families, and offer additional insight from another marginalized group that until now has not held a place in Canada’s visible and spoken history. This paper is a weaving together of three sibling narratives that were part of a panel at the Canadian Disability Studies Association (CDSA) conference in Ottawa, Ontario in June 2015. All sisters of institutionalized persons, the three contributors remark in particular on their profound experiences of loss after their brother or sister was sent away from the family home. The contributors believe that it is through the sharing of such experiences that society can better come to understand the devastation wreaked upon both individuals and families through misinformed and prejudicial policies over a period of more than 150 years.
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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 | 0.006 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.039 | 0.026 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.003 | 0.007 |
| 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".