Reality Checks: Presuming Innocence and Proving Guilt in <i>Charter</i> Welfare Cases
Bibliographic record
Abstract
Legal professionals have learned to take the neutrality and justice of laws and their underlying principles for granted: laws are presumed to be “innocent” and those who allege bias in the law are subject to the highest burden of proof. Likewise, in the social welfare context, the presumption that beneficiaries are “guilty”, while governments are innocent, pose formidable barriers to welfare recipients who claim their Charter rights have been violated. The author argues that, as the Supreme Court of Canada’s decision in Gosselin v Quebec (Attorney General) illustrates, the outcome in Charter welfare cases is dictated less by the language of Charter provisions or traditional legal analysis, as by judges' willingness to test the presumptions of innocence and guilt advanced by governments against the reality of the existing welfare system and the actual experience of welfare recipients.
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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 | 0.019 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.010 | 0.044 |
| Scholarly communication | 0.015 | 0.014 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.007 | 0.013 |
| Insufficient payload (model declined to judge) | 0.005 | 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".