Responding to Discrimination as a Function of Meritocracy Beliefs and Personal Experiences: Testing the Model of Shattered Assumptions
Bibliographic record
Abstract
We examined whether the model of shattered assumptions (Janoff-Bulman, 1992) could be applied to the reactions of victims of discrimination. Consistent with this model, it was hypothesized that those whose positive world assumptions are inconsistent with their negative experiences of discrimination would report more negative responses than those whose world assumptions match their experience. Disadvantaged group (both gender and ethnicity) members' responses to discrimination (self-esteem, collective action, intergroup anxiety) were predicted from their meritocracy beliefs and personal experiences of discrimination. Regression analyses showed a significant interaction between meritocracy beliefs and personal discrimination such that among those who reported personal discrimination, stronger beliefs that the meritocracy exists predicted decreased self-esteem and collective action as well as increased intergroup anxiety. Among those who reported little personal discrimination, stronger beliefs that the meritocracy exists predicted increased self-esteem. Implications for promoting a critical view of the social system is discussed.
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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 | 0.006 | 0.021 |
| 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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".