Bibliographic record
Abstract
Resistance and Persuasion is the first book to analyze the nature of resistance and demonstrate how it can be reduced, overcome, or used to promote persuasion. By examining resistance, and providing strategies for overcoming it, this new book generates insight into new facets of influence and persuasion. With contributions from the leaders in the field, this book presents original ideas and research that demonstrate how understanding resistance can improve persuasion, compliance, and social influence. Many of the authors present their research for the first time. Four faces of resistance are identified: reactance, distrust, scrutiny, and inertia. The concluding chapter summarizes the book's theoretical contributions and establishes a resistance-based research agenda for persuasion and attitude change. This new book helps to establish resistance as a legitimate sub-field of persuasion that is equal in force to influence. Resistance and Persuasion offers many new revelations about persuasion: *Acknowledging resistance helps to reduce it. *Raising reactance makes a strong message more persuasive. *Putting arguments into a narrative increases their influence. *Identifying illegitimate sources of information strengthens the influence of legitimate sources. *Looking ahead reduces resistance to persuasive attempts. This volume will appeal to researchers and students from a variety of disciplines including social, cognitive, and health psychology, communication, marketing, political science, journalism, and education.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.009 |
| Scholarly communication | 0.009 | 0.007 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.019 | 0.007 |
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".