The role of mindset in the accuracy and bias of relationship evaluations /
Bibliographic record
Abstract
This thesis investigated the influence of mindset on the accuracy and bias of relationship evaluations. Because deliberation about important decisions is a time when people are more realistic and impartial in processing information about themselves and the world, a deliberative mindset was expected to increase the predictive accuracy of relationship appraisals compared to an implemental mindset. Four studies using different methodologies tested this hypothesis. In Study 1, relationship mindset assessed via content coding interacted with commitment in predicting the survival of the relationship 4 months later. In Study 2, mindset assessed via closed questions about academic goals interacted with explicit forecasts in predicting the survival of the relationship 9 months later. In studies 3 and 4, an experimental manipulation of mindset interacted with explicit forecasts or relationship assessments of relationship constructs in predicting relationship survival 6 months later. Overall, a deliberative mindset increased the accuracy of relationship predictions and the validity of relationship constructs in predicting the dissolution of a relationship compared to an implemental mindset. Importantly, deliberatives were not more pessimistic than were implementals; they were more realistic. Nonetheless, a deliberative mindset about an important goal in the relationship can be threatening. Previous work found that boosting perceptions of partner superiority is one way to cope with such threat (Gagne & Lydon, 2001, Study 1). Study 5 further showed that participants in a deliberative mindset boosted their perceptions of partner superiority only if they were sufficiently committed to their relationships. Those in an implemental mindset boosted their perceptions of partner superiority irrespective of their commitment level. In sum, a deliberative mindset about the relationship increases the accuracy of evaluations serving epistemic needs (i.e., relationship predictions) while n
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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 | 0.007 | 0.057 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 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".