In Search of the Silver Lining
Bibliographic record
Abstract
Past research has demonstrated that people's need to perceive the world as fair and just leads them to blame and derogate victims of tragedy. The research reported here shows that a positive reaction--bestowing additional meaning on the lives of individuals who have suffered--can also serve people's need to believe that the world is just. In two studies, participants whose justice motive was temporarily heightened or who strongly endorsed the belief that reward and punishment are fairly distributed in the world perceived more meaning and enjoyment in the life of someone who had experienced a tragedy than in the life of someone who had not experienced tragedy, but this pattern was not found for participants whose justice motive was not heightened or who did not strongly endorse a justice belief. These results suggest that being motivated to see the world as just--a motivation traditionally associated with victim derogation--also leads people to perceive a "silver lining" to tragic events.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.004 | 0.010 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.000 | 0.006 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 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".