Attitudinal Ambivalence and Message-Based Persuasion: Motivated Processing of Proattitudinal Information and Avoidance of Counterattitudinal Information
Bibliographic record
Abstract
Attitudinal ambivalence has been found to increase processing of attitude-relevant information. In this research, the authors suggest that ambivalence can also create the opposite effect: avoidance of thinking about persuasive messages. If processing is intended to reduce experienced ambivalence, then ambivalent people should increase processing of information perceived as proattitudinal (agreeable) and able to decrease ambivalence. However, ambivalence should also lead people to avoid processing of counterattitudinal (disagreeable) information that threatens to increase ambivalence. Three studies provide evidence consistent with this proposal. When participants were relatively ambivalent, they processed messages to a greater extent when the messages were proattitudinal rather than counterattitudinal. However, when participants were relatively unambivalent, they processed messages more when the messages were counterattitudinal rather than proattitudinal. In addition, ambivalent participants perceived proattitudinal messages as more likely than counterattitudinal messages to reduce ambivalence, and these perceptions accounted for message position effects on amount of processing.
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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 | 0.004 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".