Bibliographic record
Abstract
Cet article examine les origines du stigmate et de la dicrimination et leurs repercussions majeures sur lespersonnes atteintes d’une maladie mentale, ainsi que sur leur entourage. Nous portons notre attention surles efforts qui sont faits au Canada pour reduire ce stigmate, efforts dont il n’est pas fait mention dans lesrapports du Comite permanent du Senat sur les affaires sociales, la science et la technologie. L’article setermine sur dix lecons visant a la reduction du stigmate, destinees a la fois a examiner attentivement lesexperiences canadiennes et a fournir et a orienter les futurs debats sur les politiques a suivre. Apres reflexionsur l’experience canadienne et internationale, il apparait particulierement important de reconnaitre que lescampagnes “generiques” sont, pour la plupart, inefficaces, et que les programmes doivent etre centres surdes groupes selectionnes.This paper reviews the origins of stigma and discrimination and the main consequences for people withmental illness, and those around them. Stigma reduction efforts in Canada are reviewed in light of theirabsence from the reports of the Standing Senate Committee on Social Affairs, Science and Technology. Thepaper closes with ten lessons for stigma reduction intended to both distil Canadian experiences and provideguidance for further policy debate. Reflecting on the international and Canadian experiences, of particularimportance is recognizing that generic campaigns are largely ineffective, and that programs must be carefullyfocused upon selected groups.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.017 | 0.038 |
| Scholarly communication | 0.009 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.004 | 0.006 |
| 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".