Left–right ideological differences in system justification following exposure to complementary versus noncomplementary stereotype exemplars
Bibliographic record
Abstract
Abstract The capacity for victim‐derogating stereotypes and attributions to justify social inequality and maintain the status quo is well known among social scientists and other observers. Research conducted from the perspective of system justification theory suggests that an alternative to derogation is to justify inequality through the use of complementary stereotypes that ascribe compensating benefits and burdens to disadvantaged and advantaged groups, respectively. In two experimental studies conducted in Poland we investigated the hypothesis that preferences for these two routes to system justification would depend upon one's political orientation. That is, we predicted that the system‐justifying potential of complementary versus noncomplementary stereotype exemplars would be moderated by individual differences in left–right ideology, such that left‐wingers would exhibit stronger support for the societal status quo following exposure to complementary (e.g., “poor but happy,” “rich but miserable”) representations, whereas right‐wingers would exhibit stronger support for the status quo following exposure to noncomplementary (e.g., “poor and dishonest,” “rich and honest”) representations. Results were supportive of these predictions. Implications for theory and practice concerning stereotyping, ideology, and system justification are discussed. Copyright © 2008 John Wiley & Sons, Ltd.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".