Toward a robust science of suicide: Epistemological, theoretical, and methodological considerations in advancing suicidology
Bibliographic record
Abstract
Suicidology is at a crossroads, the crux of which came into plain view recently when Hjelmeland/Knizek's critique of mainstream suicidology was followed by two ardent essays defending suicidology as it currently exists. We stake out a middle-way approach leveraging sociology's unique disciplinary perspective to bridge the two sides to construct a robust transdisciplinary toolbox that helps suicidology advance as science and improve how we study suicide and, therefore, what we know. The essay first examines the underlying structural and cultural reasons for the talking past each other, before turning towards our own understanding of science, methods, and the study of suicide.
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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 | 0.216 | 0.197 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.009 | 0.005 |
| Science and technology studies | 0.009 | 0.120 |
| Scholarly communication | 0.025 | 0.029 |
| Open science | 0.005 | 0.021 |
| Research integrity | 0.012 | 0.024 |
| 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 itClassification
machine, unvalidatedMachine predicted; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".