Compensatory conviction in the face of personal uncertainty: Going to extremes and being oneself.
Bibliographic record
Abstract
Study 1 participants' self-integrity (C. M. Steele. 1988) was threatened by deliberative mind-set (S. E. Taylor & P. M. Gollwitzer, 1995) induced uncertainty. They masked the uncertainty with more extreme conviction about social issues. An integrity-repair exercise after the threat, however, eliminated uncertainty and the conviction response. In Study 2, the same threat caused clarified values and more self-consistent personal goals. Two other uncertainty-related threats, mortality salience and temporal discontinuity, caused similar responses: more extreme intergroup bias in Study 3, and more self-consistent personal goals and identifications in Study 4. Going to extremes and being oneself are seen as 2 modes of compensatory conviction used to defend against personal uncertainty. Relevance to cognitive dissonance and authoritarianism theories is discussed, and a new perspective on terror managenment theory (J. Greenberg, S. Solomom, & T. Pyszczynski, 1997) is proposed.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".