Helping and Rationalization as Alternative Strategies for Restoring the Belief in a Just World: Evidence from Longitudional Change Analyses
Bibliographic record
Abstract
The need for justice is a core construct of Melvin Lerner's just world theory (Lerner, 1970, 1980). According to this theory, human beings want to believe that individuals get what they deserve and deserve what they get. They prefer to assume that bad deeds are punished and good deeds rewarded, and that good things happen to good people and bad things to bad people. The belief in just contingencies among character, behavior, and fate enables the person to maintain a sense of confidence and efficacy. If justice is a guiding principle in life, individuals can rely upon getting what they earn for their achievements and being respected for decency and moral integrity. This principle implies that they can prevent misfortune by conforming to social norms and ethical standards. Injustice threatens this belief system and motivates the person to engage in activities that either restore justice directly or help to maintain the belief in a just world by other means. According to Lerner and his colleagues, this can be accomplished by acting prosocially on behalf of the victim, or by cognitively restructuring the situation in such a way that it appears just (Lerner, 1980; Lerner & Miller, 1978; Lerner, Miller, & Holmes, 1976). Acknowledging injustice and actively fighting it, for example, by helping innocent victims or punishing victimizers, have been called action strategies.
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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 | 0.026 | 0.076 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.012 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| 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".