Rekeying Cultural Scripts for Youth Suicide: How Social Networks Facilitate Suicide Diffusion and Suicide Clusters Following Exposure to Suicide
Bibliographic record
Abstract
Research suggests that suicide can socially diffuse through social relationships and social contexts; however, little is known about the mechanisms that facilitate this diffusion. Using data from an in-depth case study of a cohesive community with an enduring youth suicide problem (N = 118), we examine how, after repeated exposure to suicide, the community’s cultural script for suicide may have been rekeyed such that suicide became a more imaginable option for some community youth. Essentially, we found evidence that a series of sudden, shocking, suicide deaths of high-status youth may have triggered the formation of new locally generalized meanings for suicide that became available, taken-for-granted social facts. The new meanings reinterpreted broadly shared adolescent experiences (exposure to pressure) as a cause of suicide facilitating youth’s ability to imagine suicide as something someone like them could do to escape. We conclude by discussing the implications of our findings for the scientific understanding of (1) suicide and suicide clusters, (2) social diffusion processes, and (3) suicide prevention.
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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 | 0.003 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".