Bibliographic record
Abstract
We examine whether individuals respond to comparisons involving romantic partners as they would to comparisons involving the self. Four studies (N = 2,210) using recalled (Studies 1-3) and actual (Study 4) comparisons about attractiveness (Study 1) and relationship skills (Studies 2-4) demonstrated that individuals high in self-other overlap decrease domain relevance following upward but not downward comparisons to protect their positive partner perceptions. This strategy was absent among those low in self-other overlap. Study 2 demonstrated that this effect extends to best friends, but not casual friends, due to the degree of self-other overlap. Furthermore, when reminded of their partner's inferiority in a domain, high overlap participants maintained positive global partner perceptions, whereas low overlap participants' global perceptions were negatively affected (Study 3). These results suggest that individuals do experience partner-other comparisons as if they were directly involved, but only if their partner is incorporated into their self-identity.
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How this classification was reachedexpand
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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; both teacher heads agree on what is shown here.
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".