Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 it