Power and Persuasion: Processes by Which Perceived Power Can Influence Evaluative Judgments
Bibliographic record
Abstract
The present review focuses on how power—as a perception regarding the self, the source of the message, or the message itself—affects persuasion. Contemporary findings suggest that perceived power can increase or decrease persuasion depending on the circumstances and thus might result in both short-term and long-term consequences for behavior. Given that perceptions of power can produce different, and even opposite, effects on persuasion, it might seem that any relationship is possible and thus prediction is elusive or impossible. In contrast, the present review provides a unified perspective to understand and organize the psychological literature on the relationship between perceived power and persuasion. To accomplish this objective, present review identifies distinct mechanisms by which perceptions of power can influence persuasion and discusses when these mechanisms are likely to operate. In doing so, this article provides a structured approach for studying power and persuasion via antecedents, consequences, underlying psychological processes, and moderators. Finally, the article also discusses how power can affect evaluative judgments more broadly.
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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 | 0.005 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".