Bibliographic record
Abstract
The actual language on mental health in Quebec is founded on a série of premises that generally remain implicit or are introduced as postulates of "good will" with regard to reality. The authors want to question the significance and the real range of those premises by using a comparative analysis. Considering the concept of desinstitutionalization as used in different countries, they detect the ambiguities and the differences due to the context and to the postulates specific to each of those systems. Then, to elaborate our own premises, they utilize a decentralization method: with the help of their knowledge of other cultural ways of reacting to the problems of psychiatry-mental health, on the one hand in Africa, and on the other from data gathered in Quebec from psychiatric patients, ex-patients and friends. This twofold study leads them to express, compare and criticize what they introduce as the three main premises of mental health language in Quebec: to popularize psychiatric-mental health problems and to introduce a standardization of the people and a uniformity of structure in a field that remains very complex. A study of the theoretical character of this model, of its moralizing dimension and of the key-actors who contribute to its definition, allows them to describe the socio-historic context. The authors question the possibility of introducing a new dimension in our ways of thinking, management and action.
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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 | 0.017 | 0.060 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.009 | 0.027 |
| Scholarly communication | 0.016 | 0.015 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.011 | 0.013 |
| Insufficient payload (model declined to judge) | 0.024 | 0.003 |
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".