Representations and Preferences of Responses to Housing and Employment Discrimination
Bibliographic record
Abstract
In two studies, the behavioral preferences of majority (White) and visible minority (non-White) individuals in response to a hypothetical situation of discrimination were examined. In addition, the characteristics and dimensions perceived to relate to these behaviors were also examined. In the first study, 120 primarily White undergraduate students first rated the likelihood of engaging in each of 14 behaviors in response to a situation of discrimination, and then rated each behavior on a number of attributes representing key dimensions of behavior identified in intergroup theories (individual-collective; active-passive; non-normative-normative) and phenomenological studies on the experience of discrimination (e.g. risk). A multimode factor analysis of the behaviors and attributes provided a three-component solution. While the dimensions underlying these components reflected dimensions of behavior identified by intergroup theorists, they were also qualitatively different from them. Further analysis revealed that behaviors associated with higher preference ratings were perceived as more normative, preparatory, and low in cost and risk. The behavioral preferences, and the dimensions underlying these preferences were replicated in a second study, which comprised 70 Black and South Asian participants. The patterns of results were similar for the White and non-White participants, although these two groups did differ in their endorsement and ratings of some of the behaviors.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".