Taxonomies of psychological individual differences: biological perspectives on millennia-long challenges
Bibliographic record
Abstract
This Editorial highlights a unique focus of this theme issue on the biological perspectives in deriving psychological taxonomies coming from neurochemistry, neuroanatomy, neurophysiology, genetics, psychiatry, developmental and comparative psychology-as contrasted to more common discussions of socio-cultural concepts (personality) and methods (lexical approach). It points out the importance of the distinction between temperament and personality for studies in human and animal differential psychophysiology, psychiatry and psycho-pharmacology, sport and animal practices during the past century. It also highlights the inability of common statistical methods to handle nonlinear, feedback, contingent, dynamical and multi-level relationships between psychophysiological systems of consistent psychological traits discussed in this theme issue.This article is part of the theme issue 'Diverse perspectives on diversity: multi-disciplinary approaches to taxonomies of individual differences'.
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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 | 0.041 | 0.036 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.001 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.006 | 0.066 |
| Scholarly communication | 0.012 | 0.039 |
| Open science | 0.004 | 0.019 |
| Research integrity | 0.006 | 0.022 |
| 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".