Bibliographic record
Abstract
RiSUMEkAu cours de la derni~re d~cennie, le probl~me de mendicit6 dans les grandes villes aux ttats-Unis et au Canada a entrain6 une augmentation des mesures l6gislatives prises par les municipalit~s, les ttats et les provinces.Cet article d~crit les contestations juridiques relatives aux lois antimendicit6 pr~sentement en cours au Canada, analyse les revendications canadiennes en vertu de la Charte canadienne des droits et liberts et pr~sente les r~sultats de contestations semblables aux ttats-Unis.Dans cet article, l'auteure indique que les motifs de telles lois constituent un < mythe de la beaut6 >> de l'ordre public imposd par les gouvernements qui ont, par des compressions draconiennes dans les programmes sociaux, engendr6 le probl~me qu'ils tentent maintenant de r6gler.L'auteure insiste sur le fait que les lois antimendicit6 criminalisent les pauvres, censurent la critique des probl~mes sociaux et font en sorte que les rues et les endroits publics sont visuellement nettoy~s au detriment de la population en g~n~ral.L'article examine comment de telles interventions lgislatives entrent en conflit grave avec les valeurs de la libert6 d'expression, des libert6s individuelles et de la dignit6 humaine qui sous-tendent la Charte, et se termine par un aperqu du traitement r~serv6 par les tribunaux des 8tats-Unis A des causes similaires.I.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.032 | 0.014 |
| Scholarly communication | 0.012 | 0.004 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.029 | 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".