25 Years of the <em>Canadian Journal of Community Mental Health</em>: Reflections and Future Directions
Bibliographic record
Abstract
As a former senior editor, a long-term member of the editorial board, and a regular contributor to the Canadian Journal of Community Mental Health (CJCMH), I am delighted with the invitation to comment on the 25-year review of the contents of CJCMH by Fortin-Pellerin, Pouliot-Lapointe, Thibodeau, and Gagné. I want to begin by acknowledging that I cannot pretend to give an objective account of CJCMH, not only because of my deep investment in it, but also because of my personal and professional biases, values, and position of privilege. What I can offer are some reflections on CJCMH, as well as suggestions for future directions, from my perspective as a senior academic community psychologist.
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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 | 0.043 | 0.077 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.012 | 0.013 |
| Scholarly communication | 0.019 | 0.012 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.017 | 0.021 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".