Experimental Research on Just-World Theory: Problems, Developments, and Future Challenges.
Bibliographic record
Abstract
M. J. Lerner (1980) proposed that people need to believe in a just world; thus, evidence that the world is not just is threatening, and people have a number of strategies for reducing such threats. Early research on this idea, and on just-world theory more broadly, was reviewed in early publications (e.g., M. J. Lerner, 1980; M. J. Lerner & D. T. Miller, 1978). In the present article, focus is directed on the post-1980 experimental research on this theory. First, 2 conceptualizations of the term belief in a just world are described, the typical experimental paradigms are explained, and a general overview of the post-1980 experiments is provided. Second, problems with this literature are discussed, including the unsystematic nature of the research. Third, important developments that have occurred, despite the problems reviewed, are described. Finally, theoretical challenges that researchers should address if this area of inquiry is to advance in the future are 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 | 0.153 | 0.256 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.003 | 0.024 |
| Scholarly communication | 0.006 | 0.029 |
| Open science | 0.006 | 0.007 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.010 | 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".