Inferring a partner’s ideal discrepancies: Accuracy, projection, and the communicative role of interpersonal behavior.
Bibliographic record
Abstract
Guided by the ideal standards model (Simpson, Fletcher, & Campbell, 2001), we tested in 2 studies whether (a) individuals were accurate when inferring how closely they matched their romantic partner's ideal standards, (b) such accurate inferences explained why people are more satisfied when they more closely match their partner's ideals, and (c) accurate inferences are generated via the partner's behavior during conflict interactions. Both members of dating and/or married couples were recruited for each study. In both studies, people's inferences into how closely they matched their partner's ideals were based on a blend of accuracy and projection processes. Individuals were also less satisfied when they failed to match their partner's ideal standards (as rated by their partner), and, as predicted, this effect was mediated by people's accurate inferences regarding how closely they matched their partner's ideals. In Study 2, spouses were also video-recorded while they attempted to resolve an important marital conflict. As predicted, Partner A's prediscussion ideal discrepancies predicted pre- to postdiscussion changes in Partner B's inferences, and this effect was partly mediated by the observed interpersonal behaviors of Partner A. Results from these dyadic data analyses suggest that people do have accurate insight into the extent to which they match their partner's ideal standards, and these inferences are generated, in part, by the way the partner behaves toward the self during diagnostic conflict interactions.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.010 | 0.079 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 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".