Uncertainty Orientation in the Group Context: Categorization Effects on Persuasive Message Processing
Bibliographic record
Abstract
Persuasive messages often originate from in-group or out-group sources. Theoretically, in-group categories could facilitate heuristic-based message processing (because of the attractiveness of in-groups and their social reality cues) or systematic-based processing (because of high personal relevance of the message). The authors expected individual differences in uncertainty orientation and socially based expectancy congruence to be important variables in understanding these processes. Participants were exposed to strong or weak, in-group or out-group messages that were either expectancy congruent (in-group agreement, out-group disagreement) or expectancy incongruent (in-group disagreement, out-group agreement). As predicted, uncertainty-oriented participants increased systematic information processing under incongruent conditions relative to congruent (i.e., relatively certain) conditions; certainty-oriented individuals processed systematically only under congruent conditions. These findings suggest that uncertainty that has been created through social-categorization conflicts is treated differently by people of different personality styles.
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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 | 0.018 |
| 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 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".