Do you really understand? Achieving accuracy in interracial relationships.
Bibliographic record
Abstract
Accurately perceiving whether interaction partners feel understood is important for developing intimate relationships and maintaining smooth interpersonal exchanges. During interracial interactions, when are Whites and racial minorities likely to accurately perceive how understood cross-race partners feel? We propose that participant race, desire to affiliate, and racial salience moderate accuracy in interracial interactions. Examination of cross-race roommates (Study 1) and interracial interactions with strangers (Study 2) revealed that when race is salient, Whites higher in desire to affiliate with racial minorities failed to accurately perceive the extent to which racial minority partners felt understood. Thus, although the desire to affiliate may appear beneficial, it may interfere with Whites' ability to accurately perceive how understood racial minorities feel. By contrast, racial minorities higher in desire to affiliate with Whites accurately perceived how understood White partners felt. Furthermore, participants' overestimation of how well they understood partners correlated negatively with partners' reports of relationship quality. Collectively, these findings indicate that racial salience and desire to affiliate moderate accurate perceptions of cross-race partners-even in the context of sustained interracial relationships-yielding divergent outcomes for Whites and racial minorities. (PsycINFO Database Record (c) 2015 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 | 0.003 | 0.024 |
| 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 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 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".