The Just World and Winston Churchill: An Approach/Avoidance Conflict about Psychological Distance When Harming Victims
Bibliographic record
Abstract
How do people respond when they observe a victim's undeserved suffering, such as when competent, hardworking employees are laid off for reasons that have nothing to do with their performance? Lerner's famous just world hypothesis (e.g., Lerner, 1980; Lerner & Simmons, 1966) addressed reactions by non-harmdoing observers, such as outsiders who learn about the suffering of laid-off employees. We instead address the case of harmdoing observers, such as the managers who lay off the employees. In the Lerner analysis, witnesses to a victim's undeserved suffering “will be increasingly likely to reject that victim as a function of the degree of injustice associated with the victim's fate” (Lerner, 1980, p. 56). Because victims received derogatory ratings under the predicted conditions, this form of rejection is known as victim derogation. Other possible responses – such as prevention, restitution, and acceptance of one's limitations – Lerner called rational tactics. Non rational tactics that involve variations on the theme of ego defense, of course, constitute the more interesting category from a psychological perspective. These include denial/withdrawal and reinterpretation. Derogating a victim is a special case of the latter – namely, a reinterpretation of the victim's character. Other psychological defenses in the same category (nonrational tactics) include reinterpreting the outcome and reinterpreting the cause. In the original Lerner and Simmons (1966) paradigm, the observer was innocent and powerless.
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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 | 0.004 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.006 | 0.010 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 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".