Relationship-specific identification and spontaneous relationship maintenance processes.
Bibliographic record
Abstract
Attractive alternative partners pose a relational threat to people in romantic relationships. Given that people are often limited in their time and energy, having the capacity to effortlessly respond to such relational threats is extremely useful. In 4 studies, we explored how people's identity in terms of their romantic relationship--their relationship-specific identity--affects their relationship-protective behaviors. We predicted that once a relationship becomes a part of one's sense of self, relationship maintenance responses are exhibited in a relatively fluid, spontaneous manner. In Study 1, we assessed the convergent and divergent validity of relationship-specific identification, demonstrating how it is associated with other relationship constructs. In Study 2, we found that less identified participants mentioned their relationship less than those high in relationship-specific identification, but only when interacting with an attractive member of their preferred sex. In Study 3, using a dot-probe visual cuing task, we found that when primed with an attractive member of their preferred sex, those low in relationship-specific identification gazed longer at attractive preferred-sex others compared to those high in relationship-specific identification. In Study 4, we found that relationship-specific identification was associated with relationship survival 1-3 years after the initial assessment. The present results demonstrate that relationship-specific identification predicts relatively spontaneous, pro-relationship responses in the face of relational threat.
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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 | 0.001 | 0.011 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".