How enforcement shapes compliance with legal rules: the case of long-term care homes in Ontario
Bibliographic record
Abstract
Abstract This paper contributes to the legal and socio-legal literature on long-term care (LTC) facilities (also known as nursing homes) by drawing from the responsive regulation literature and empirical research conducted in 2021 and 2022. Enforcement is an under-explored aspect in the legal and socio-legal literature on LTC. This research asks how the regulator’s enforcement activities shape compliance of LTC homes in Ontario. This paper reports the results from eleven semi-structured key informant interviews with associations that represent LTC facilities, advocacy organizations, unions, and professionals, such as lawyers. The current enforcement activities do not appear to evoke responsiveness in at least some of the LTC homes because the regulator’s approach is not dynamic: the regulator does not change its mix of “persuasion” and “coercion” in order to respond to the motivations and behaviours of homes. Inspection and enforcement activities have had little impact on how homes respond to rules.
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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 | 0.005 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.022 | 0.012 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| 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".