Depicted Immorality Influences the Perceived Applicability of the Phrase "Committed Suicide"
Bibliographic record
Abstract
Abstract. Background: "Committed suicide" is often deemed less acceptable than alternative phrases, but such judgments vary widely across individuals. Aim: We tested whether the endorsement of statements containing "committed suicide" is greater when a suicide death is depicted as immoral. We also assessed the degree of immorality suggested by the free-standing phrases "committed suicide" and "died by suicide." Method: Undergraduate participants ( N = 154) read scenarios of a suicide depicted as immoral and one depicted more neutrally and judged the applicability of statements employing either "committed suicide" or "died by suicide" to each scenario. Participants next chose between the free-standing phrases "committed suicide" or "died by suicide" in terms of which connoted immorality and provided written justifications for their choices. Results: Participants judged "committed suicide" statements to be most applicable to the immoral-suicide scenario. A large majority of participants chose "committed suicide" over "died by suicide" as connoting immorality and participants' justifications for this choice revealed several meaningful themes. Limitations: Our manipulation of immorality employed religious overtones and our participants were undergraduate students. Conclusions: Findings contribute to the empirical basis for concerns regarding the phrase "committed suicide," with implications for stigma reduction and help-seeking.
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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 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".