Correlations of Fundamental Social Motives with Personality Measures and Life History Variables
Bibliographic record
Abstract
Background: In response to the replication crisis in the field of psychology, the authors conduct a replication of the Neel et al. (2016) (1) study examining individual differences in fundamental social motives. Methods: Using the Fundamental Social Motives Inventory, we explore the relationships of the fundamental social motives to other individual differences and personality measures and the extent to which life history variables (e.g., age, sex, childhood environment) predict individual differences in the fundamental social motives. In addition to the replication study, the authors also incorporate the Behavioral Inhibition/Activation Scale (BIS/BAS) as a new variable to determine this measure of personality’s correlation with all seven fundamental social motives of Self-Protection, Disease Avoidance, Affiliation, Status, Mate Seeking, Mate Retention, and Kin Care. A total of 34 participants are recruited from Amazon Mechanical Turk to complete the measures of personality under question. The replication criteria are set at ±0.15 r/β-units from the original study results and effect sizes greater than or equal to r/β=0.5 have to demonstrate statistical significance at the p<0.05 level.Results: Results demonstrate that between a third and a half of all effect sizes replicate Neel et al.’s (1) findings.Limitations: These results should be considered carefully with respect to the low sample size of our study.Conclusion: The BIS/BAS variable proves to be most informative, indicating that the seven motives cluster under either the BIS or BAS factors with medium to large strengths of correlation. These findings contribute to discussions on considering the most accurate measures of social motivation and the implications of individual differences in psychology’s understanding of such motivational systems.
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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 | 0.003 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".