The pursuit of self-interest: Self-interest bias in attitude judgment and persuasion.
Bibliographic record
Abstract
Self-interest affected the direction of attitudes in 4 studies exploring attitude judgment and persuasion. Experiment 1 showed that both self-interest and symbolic concerns predicted attitudes. The biasing role of self-interest in producing the well-known persuasion effects of personal relevance and argument strength was examined by disentangling the competing effects of personal costs and benefits. Experiment 2 used a standard personal relevance manipulation in the absence of supportive arguments and showed that perceptions of personal costs associated with the advocated policy partially mediated its negative effects on attitudes. Experiments 3 and 4 independently manipulated the onset of personal costs associated with an issue and the onset of issue-related benefits conveyed by supportive arguments. Postmessage attitudes were an additive function of personal costs and argument-specified benefits, and perceived costs and benefits biased information processing in a self-interested manner. A revised conception of personal relevance and argument strength is 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 | 0.007 | 0.035 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".