The Development and Validation of Statistical Prediction Rules for Discriminating Between Genuine and Simulated Suicide Notes
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The suicide note is a valuable source of information for assisting police forces in equivocal death investigations. The present study endeavored to develop statistical prediction rules to discriminate between genuine and simulated suicide notes. Discriminant function analysis was performed on a sample of 33 genuine and 33 simulated notes to identify variables that serve as best predictors of note authenticity. Receiver operating characteristic analysis was then applied to validate these models and establish decision thresholds. The optimal model yielded an accuracy score of .82, with average sentence length and expression of positive affect being particularly effective at discriminating between the notes. Theoretical implications are discussed as are the practical advantages of applying receiver operating characteristic analysis in the investigation of equivocal deaths.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it