Mitigating Social Inequities in Quebec: Governance Law to the Rescue?
Bibliographic record
Abstract
All over the world as in Quebec, the COVID-19 crisis forced the government to declare a state of public health emergency. Under this exceptional regime, decision-making is extremely centralized and is more based on a top-down approach. The Government of Quebec has thus ordered public health measures of an exorbitant scope, applying to all citizens regardless of their particular living conditions. Certain measures, for example, curfews, have thus created or exacerbated social inequalities, particularly in terms of exposure to the risk posed by COVID-19, access to healthcare or educational services or the ability to comply with certain health instructions, adding a burden for populations that are often already vulnerable. To mitigate this phenomenon, bottom-up initiatives addressing social inequities have emerged in the margins of state action; other initiatives bringing stakeholders together to find solutions have been demanded by the state. Taking a few of those initiatives as examples (groups with food insecurity, victims of domestic violence, people experiencing homelessness), the authors propose an analysis of this governance born during the crisis to remedy the shortcomings of state law, paying particular attention to the norms developed by the participating actors to organize their actions.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.011 | 0.009 |
| Scholarly communication | 0.007 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".