Everyone I Don’t Like Is Biased: Affective Evaluations and the Bias Blind Spot
Bibliographic record
Abstract
People commonly exhibit a bias blind spot (BBS), judging themselves as less susceptible to bias than the "average other." However, less is known about how people attribute bias to familiar others who evoke strong affect. We examined whether attributions of bias are sensitive to affective impressions of others. In Experiment 1, participants viewed themselves as considerably less biased than the average survey respondent and a personally-known disliked other, but not less biased than a familiar individual whom they liked. Experiments 2 and 3 examined the BBS in politically polarized groups of Democrats and Republicans. While participants judged themselves as somewhat less biased than co-partisans, they viewed themselves as much less biased than their political opponents. In all experiments, the effect of other target selection on the BBS was mediated by affective evaluations. We discuss the theoretical implications of affective evaluations guiding how people attribute bias to familiar others.
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 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 | 0.011 | 0.001 |
| 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 | 0.002 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".