Do regional suicide rates predict reproductive fitness among those with mental health conditions? Presenting operational calibrations for testing the theory of psychological aposematism
Bibliographic record
Abstract
Psychological aposematism suggests that suicide operates as a conditioning mechanism for society. If bereavement and economic impacts from several suicides under similar contexts are experienced by a community, that community may be prompted to alleviate said contexts. This process may prove evolutionarily adaptive if translating into the relief of low-fitness contexts for suicidal individuals. A prediction of this theory is that geographical regions with higher suicide rates will exhibit higher reproductive fitness among those who suffer from suicidality or mental illness. This hypothesis is examined using limited data, from a combination of Statistics Canada data sources. The provinces of Canada (n = 10) were used across 2 time-points (2016 and 2021 Census cycles) to explore and calibrate possible statistical tests of the stated hypothesis. Suicide rates preceding Census years were used to predict changes in reproduction among those reporting a mental health condition versus those reporting no mental health condition. Several operationalisations of stated concepts were used, with favourability of results being judged by the degree of agreement between differential approaches (referred to as coherency). Some observed data patterns appear favourable towards the given hypothesis, though results are not statistically significant and are not always coherent with results observed under differential operationalisations. Results will receive continued re-evaluation as new Census and suicide rate data become available.
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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 | 0.004 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".