Papageno Stories Predict Lower Suicide Rates – Analysis of American Feature Films, 1950–2002
Bibliographic record
Abstract
Abstract: Background: A majority of research concerning media impacts on suicide has focused on the harmful impacts. In contrast, the present study focuses on positive media impacts, the Papageno effect. The central hypothesis is that the higher the exposure of the public to films portraying a story of a suicidal person who ultimately recovers, the lower the suicide rate. Methodology: Data on suicides per 100,000 were from the US Public Health Service. Seven online film bibliographies were searched to include American films that (1) contained a person initially attempting suicide and then conquering their problems and (2) were in the top 50 at the box office. The number of such portrayals per year comprised the chief independent variable, while adjustments were made for three core theoretical constructs. Results: Sixty-one narratives met the inclusion criteria. An AR-1 Cochrane–Orcutt multivariate analysis showed that controlling for the other predictors, each additional exposure to a Papageno story significantly decreased the suicide rate ( b = −.059, SE = .023, t = −2.51, p = .015). The full model explained 89% of the variance. Limitations: Only half a century was assessed. Conclusion: This is the first analysis linking the yearly frequency of Papageno narratives to a lower national suicide rate.
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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 | 0.000 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 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".