Addressing Systemic Abuse in Quebec Long-Term Care Homes: The Class Action Solution
Bibliographic record
Abstract
Abstract: The COVID-19 pandemic has shined a spotlight on the systemic inequalities facing elderly residents in long-term care facilities. In the Quebec context, the province’s class action scheme goes much beyond a mere procedural tool but offers a critical avenue towards challenging the pervasive harms that are engrained within the dominant practices and norms of long-term care facilities. Dealing with systemic discrimination requires giving victims a platform to voice their experiences with discrimination and allowing them to take an active role in the process of making change. I argue the class action mechanism’s inherent goals towards collective justice provide the potential to give victimized elderly populations the ability to both voice their negative experiences in long-term care facilities and challenge these inequalities. Alongside Quebec’s Charter of Human Rights and Freedoms and punitive remedies, the plaintiff-centred process for authorizing a class action helps remove barriers that commonly limit elderly populations from engaging with the legal system. By using the class action mechanism in greater frequency, the elderly population will be able to challenge the abuses of large operators of long-term care facilities and pressure change.
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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 | 0.016 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.006 | 0.005 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 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".