Body mass is linked with a broad range of personality nuances, but especially those with behavioral content: A multi-sample exploration
Bibliographic record
Abstract
Various personality domains and facets correlate with body mass index (BMI), but recent studies suggest that using narrower personality traits—nuances—could contribute to a more detailed understanding of personality–body weight associations. We used three large datasets with different inventories to describe nuances’ correlations with BMI and explore whether BMI predominantly correlated with affective, behavioral, cognitive, or motivational item content. BMI correlated with many nuances, most prominently those reflecting immoderation, lack of orderliness, talkativeness, leadership tendencies, anger, traditionalism, and preference for routine. The highest nuance-level correlation was .21, compared to .11 for the Five-Factor Model domains. BMI correlated most strongly with nuances predominantly reflecting behaviors. Nuance-based approaches can thus reveal the strength, multitude, and content-nature of personality–outcome correlations that can potentially remain hidden in broader traits. If personality traits become relevant in the prevention or treatment of obesity, a focus on narrow behavioral traits may be especially warranted.
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| 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 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".