Trait‐Specific Dependence in Romantic Relationships
Bibliographic record
Abstract
Informed by three theoretical frameworks--trait psychology, evolutionary psychology, and interdependence theory--we report four investigations designed to develop and test the reliability and validity of a new construct and accompanying multiscale inventory, the Trait-Specific Dependence Inventory (TSDI). The TSDI assesses comparisons between present and alternative romantic partners on major dimensions of mate value. In Study 1, principal components analyses revealed that the provisional pool of theory-generated TSDI items were represented by six factors: Agreeable/Committed, Resource Accruing Potential, Physical Prowess, Emotional Stability, Surgency, and Physical Attractiveness. In Study 2, confirmatory factor analysis replicated these results on a different sample and tested how well different structural models fit the data. Study 3 provided evidence for the convergent and discriminant validity of the six TSDI scales by correlating each one with a matched personality trait scale that did not explicitly incorporate comparisons between partners. Study 4 provided further validation evidence, revealing that the six TSDI scales successfully predicted three relationship outcome measures--love, time investment, and anger/upset--above and beyond matched sets of traditional personality trait measures. These results suggest that the TSDI is a reliable, valid, and unique construct that represents a new trait-specific method of assessing dependence in romantic relationships. The construct of trait-specific dependence is introduced and linked with other theories of mate value.
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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 | 0.002 | 0.009 |
| 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 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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 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".