Minority Versus Majority Influence and Uncertainty Orientation: Processing Persuasive Messages on the Basis of Situational Expectancies
Bibliographic record
Abstract
The authors examined the effects of uncertainty orientation on processing persuasive messages from minority sources versus majority sources. The authors gave participants a proattitudinal or counterattitudinal message that either a numerical majority or a numerical minority endorsed and that contained strong or weak arguments. In support of the hypothesis that was related to message scrutiny, uncertainty-oriented individuals engaged in greater message scrutiny when the Source-Position (i.e., minority/majority-pro/con) pairing was imbalanced (in majority-con, minority-pro conditions) than when it was balanced (in majority-pro, minority-con conditions). Certainty-oriented participants showed the opposite pattern, scrutinizing the message more when the situation was balanced than when the situation was imbalanced. Support for the hypothesis that was related to nonsystematic processing was less clear because the majority appeared to have played a greater role in accounting for the aforementioned interaction than did the minority. Additional analyses supported this interpretation. However, in all cases, individual differences in uncertainty orientation moderated strength and direction of information 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 | 0.002 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".