False-Positives in Psychopathy Assessment: Proposing Theory-Driven Exclusion Criteria in Research Sampling
Bibliographic record
Abstract
Recent debates in psychopathy studies have articulated concerns about falsepositives in assessment and research sampling.These are pressing concerns for research progress, since scientific quality depends on sample quality, that is, if we wish to study psychopathy we must be certain that the individuals we study are, in fact, psychopaths.Thus, if conventional assessment tools yield substantial false-positives, this would explain why central research is laden with discrepancies and nonreplicable findings.This paper draws on moral psychology in order to develop tentative theory-driven exclusion criteria applicable in research sampling.Implementing standardized procedures to discriminate between research participants has the potential to yield more homogenous and discrete samples, a vital prerequisite for research progress in etiology, epidemiology, and treatment strategies.
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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 | 0.483 | 0.689 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.006 | 0.003 |
| Science and technology studies | 0.004 | 0.016 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.007 | 0.010 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".