Perceptions of a romantic partner’s approach and avoidance motives: Accuracy, bias, and emotional cues.
Bibliographic record
Abstract
We examined tracking accuracy and bias (mean-level and projection) in people's perceptions of their romantic partner's relationship approach and avoidance motives, similarity in partners' motives, and positive and negative emotions as potential cues used to make judgments about a partner's daily motives and motives during shared activities. Using data from 2 studies, 1 using daily diaries (N = 2,158 daily reports), the other using reports of shared activities (N = 1,228 activity reports), we found evidence of tracking accuracy and projection across samples; we also found evidence of mean-level bias such that people underperceived their partner's approach (daily) and avoidance motives (daily and in shared activities). Partners had similar daily approach and avoidance motives but were not similar in their motives during shared activities. Further, our studies indicated that emotions often serve as relevant, available, and detectable cues for judging a partner's motives. The results demonstrate that accuracy and bias are both present in judgments of a romantic partner's approach and avoidance motives, and that people often, but not always, use their partner's emotions to make such judgments. (PsycInfo Database Record (c) 2020 APA, all rights reserved).
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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 | 0.004 | 0.031 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".