Personal and Group Relative Deprivation: Connecting the ‘I’ to the ‘We’
Bibliographic record
Abstract
When it was recognized that some groups (e.g., women and African Americans) were unfairly treated in the workforce, the Canadian and American governments, to name a few, took action via affirmative action programs to remedy the situation. These programs include strategies to eliminate systemic barriers and achieve fair representation of target group members at all levels and in every sector of the labor market. This entails profound social changes as members of designated groups access jobs they were previously denied on the basis of such characteristics as race or sex. In this chapter, we focus on responses to programs designed to improve the conditions of women in the workforce. It will be shown that the attitudes of both those who might gain from the introduction of social change, the disadvantaged group (e.g., women), and those who might lose some longstanding privileges, the advantaged group (e.g., men), can be explained by referring to the concept of relative deprivation. By doing so, this chapter deals with questions raised by Pettigrew (1967) about the consequences of social evaluations. Indeed, relative deprivation refers to the affective reactions to disadvantageous personal and group comparisons. The comparisons identified by Walker and Pettigrew (1984), and believed to be important in the development of feelings of relative deprivation, are pivotal in the reactions of all parties concerned by the introduction of social change.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.012 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 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; 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".