Intelligence and variability in personality
Bibliographic record
Abstract
Using archival data, this study tests the Differentiation of Personality by Intelligence (DPI) Hypothesis based on responses from 794 mature students with an average age of 29.74 years ( SD = 2.67). Individuals completed three intelligence measures and an omnibus personality scale measuring 16 personality traits. For each intelligence measure, the sample was split into tertiles and the variability of each of the personality trait scales were compared between the higher versus lower intelligence scoring thirds. Based on variance ratio tests, 29 of the 48 comparisons (60 %) suggested some support for the DPI Hypothesis, although most of the comparison tests (79 %) were statistically non-significant. The results were compared to a previous study examining the DPI Hypothesis with the same personality scale, and in general, the results failed to replicate, with only seven of the 16 findings replicating. As the DPI Hypothesis is typically supported with other personality measures, and not supported across all measures, we suggest that there may be an influence from the personality scale item content and provide a suggestion of how to test this possible influence.
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How this classification was reachedexpand
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 | 0.000 |
| 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) | 0.002 | 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 teacher head, 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".