Bibliographic record
Abstract
Melvin Lerner's concept of the justice motive has made a major contribution to social psychology. His observations – supported by a raft of elegant experiments – about how people like to think that they are just and like to imagine that they live in a just world have profoundly influenced the shape of social psychology in America. Among the many psychologists to follow the path laid down by Lerner is the first author of this piece, Faye Crosby. Our chapter contains three parts. In the first, we review briefly Mel Lerner's ground-breaking scholarship on “the fundamental delusion,” the need to believe that one's world is fair. The second part of the chapter explores one elaboration of the phenomenon: the denial of personal discrimination. In the second part, we see that people (or at least people in our culture) defend more against recognizing injustices to the individual than against recognizing injustices to groups. People may be particularly resistant to seeing the self as the victim of an injustice. In the final section of the chapter, we peek at preliminary findings of an ethno-methodological study of women who have awakened to discrimination and are now seeking legal means to reestablish justice in their worlds. Throughout the chapter, we see that as the focus of attention (on the self, on another, or on groups) changes, so do the ways that individuals make decisions about justice.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.004 | 0.010 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".