Bibliographic record
Abstract
Abstract In May 1993, the United Nations convened a meeting of fifteen experts from twelve countries (Australia, Canada, China, Estonia, Finland, Hungary, India, Japan, Netherlands, Nigeria, United Arab Emirates and the United States) to draft guidelines for the development of national strategies for the prevention of suicidal behaviours (Ramsay and Tanney 1996). These guidelines were subsequently published as Prevention of suicide: guidelines for the formulation and implementation of national strategies (United Nations 1996). The UN Guidelines emphasized that the development of a national strategy required: 1 A government-initiated national policy that declares suicide prevention as a public health priority; 2 Broad involvement from different sectors and segments of society, and 3 The establishment of a coordinating body to formulate and implement the strategy (Ramsey 2001). In 1997, following the United Nations Guidelines, advocates pressed for resolutions to be introduced in the 105th Congress of the United States to recognize suicide as a national problem, worthy of a national solution, and calling for the development of a national strategy. Both resolutions specifically urged the development of ‘an effective national strategy for the prevention of suicide’, and were critical steps in moving suicide prevention efforts in the United States forward.
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.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 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".