Collaborative knowledge-making in the everyday practice of youth suicide prevention education
Bibliographic record
Abstract
The development and implementation of a new school‐based suicide prevention education programme in one secondary school in Vancouver, British Columbia, recently provided us with an opportunity to conduct an in‐depth, qualitative case study. The purpose of our study was to deepen our understanding of how school‐based suicide prevention education programmes like this one get planned and enacted in particular, local settings. We argue that the narrow range of methodologies that have traditionally been deployed to study school‐based youth suicide prevention education programmes have hindered our ability to see the complexities and potentialities of this work. Through the presentation of a sub‐set of findings, we aim to show the possibilities for fresh thinking and contextualized understandings that a qualitative case study, informed by a constructionist methodology, invites.
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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 | 0.032 | 0.038 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.018 | 0.047 |
| Scholarly communication | 0.016 | 0.010 |
| Open science | 0.004 | 0.018 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".