Huronia's Double Bind: How Institutionalisation Bears Out on the Body
Bibliographic record
Abstract
Frequently missing from histories of forced institutionalisation are close readings of the enduring impact on survivors' corporeality. In this article the authors analyse interview data featuring people who survived the Huronia Regional Centre: a total institution designed to warehouse people with intellectual disabilities that operated in Canada from 1876 to 2009. These interviews reveal the impact of institutional technologies on the bodies of the institutionalised, and how institutional survivors resisted those technologies. Institutional rituals meant to organise and cleanse residents, resulted in the reification of institutional subjects as inescapably contaminated. Drawing from Mary Douglas's theory of dirt and Julia Kristeva's interpretation of dirt as abjection, the authors engage with interview data on daily institutional care routines, particularly dressing, eating, showering, and the administration of medication, to show how these rituals produced for the institutionalised subject meanings around gender and disability as markers of defilement. The authors argue that the kinds of deeply oppressive and often violent rituals central to lived experiences of institutionalisation are grounded in the assumption that disabled gendered bodies are already-abject, hence the institutional demand for the institutionalised to be brought under control.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.019 | 0.096 |
| Scholarly communication | 0.010 | 0.007 |
| Open science | 0.002 | 0.013 |
| Research integrity | 0.002 | 0.004 |
| 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".