Using the 16PF to Test the Differentiation of Personality by Intelligence Hypothesis
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The differentiation of personality by intelligence hypothesis suggests that there will be greater individual differences in personality traits for those individuals who are more intelligent. Conversely, less intelligent individuals will be more similar to each other in their personality traits. The hypothesis was tested with a large sample of managerial job candidates who completed an omnibus personality measure with 16 scales and five intelligence measures (used to generate an intelligence g-factor). Based on the g-factor composite, the sample was split using the median to conduct factor analyses within each half. A five-factor model was tested for both the lower and higher intelligence halves and were found to have configural invariance but not metric or scalar invariance. In general, the results provide little support for the differentiation hypothesis as there was no clear and consistent pattern of lower inter-scale correlations for the more intelligent individuals.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.004 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it