Behavioural-Genetic Perspectives on Personality Function
Bibliographic record
Abstract
In the wake of the recent announcements that the human genome has been mapped, efforts to identify the genetic loci underlying personality function will grow and intensify. Much research has already been done in this area, but it has for the most part been limited to classical biometrical approaches designed to determine if personality has a heritable basis. These so-called "heritability" studies estimate how much of the individual differences in personality are attributable to genetic differences among people. Molecular-genetic approaches, on the other hand, are designed to identify specific putative loci, but have yielded mixed results. The inconsistency in research findings can be attributed in part to the lack of sufficient numbers of genetic markers in the chromosomal regions of interest--a problem that the creation of a map of the human genome will help to rectify. This map and its inevitable refinements, however, can only advance the search for the genes for personality to a limited degree. Serious unresolved problems in the conceptualization and definition of personality and its dysfunction remain, which will hamper the search for personality genes.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".