Introduction: rethinking suicide
Bibliographic record
Abstract
Throughout the world, suicides account for a significant number of premature deaths each year. According to the World Health Organization (WHO), one million people die by suicide annually, representing a global mortality rate of 16/100,000 (WHO, 2013). Each suicide is estimated to personally affect at least seven individuals (Canadian Association for Suicide Prevention, 2004). Suicide, like many other complex social problems, is often a subproblem of other, larger problems (Brown, Harris, and Russell, 2010). For example, newspaper headlines such as “Greek woes drive up suicide rate” (Smith, 2011) or “Rape, bullying led to N.S. teen’s death says mom” (Canadian Broadcasting Corporation, 2013) attest to the fact that suicide cannot be easily understood in singular, static, or acontextual terms. On the contrary, suicide and suicidal behaviours are deeply embedded in particular social, political, ethical, and historical contexts. As such, they are rarely amenable to cause–effect reasoning, quick fixes, or technical solutions. In short, suicide is a complex problem that is always “on the move.” Not surprisingly, given its complexity, the evidence about how to prevent suicide and suicidal behaviours is rather sparse (DeLeo, 2002; Gould and Kramer, 2001; Mann et al., 2005; Thompson, 2005). We contend that this provides an opening for fresh thinking and justifies the consideration of alternative approaches.
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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 | 0.006 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.006 | 0.018 |
| Scholarly communication | 0.010 | 0.014 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.007 | 0.011 |
| Insufficient payload (model declined to judge) | 0.026 | 0.010 |
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".