Bias and Accuracy in Close Relationships: An Integrative Review
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Intimates typically are positively biased in their relationship evaluations. Given this fact, how can intimates regulate their esteem needs about their relationships and still function effectively, without risking later regret and disappointment? We address this issue by first reviewing work showing that because bias and accuracy are independent, they can co-exist. We next show how bias and accuracy are subject to different evaluative motives, relationship evaluations, and situations. It is argued that the pursuit of important goals is a time when people are motivated to feel good about their relationships. This is a time when relationship judgments are positively biased and relatively inaccurate. However, important choice points in the relationship are times when people are motivated to both accurately understand their relationships and to feel good about their relationships. These dual needs can be simultaneously met by becoming more accurate in epistemic-related relationship judgments while being more positively biased in esteem-related relationship judgments.
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.
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 | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.003 |
| 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 it