The Elements of Political Persuasion: Content, Charisma and Cue
Why this work is in the frame
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Bibliographic record
Abstract
Political campaigns employ complex strategies to persuade voters to support them. We analyse the contributions of elements of these strategies using data from a field experiment that randomly assigned canvassers to districts, as well as messaging and endorsement conditions. We find evidence for a strong overall campaign effect and show effects for both message-based and endorsement-based campaigns. However, we find little evidence that canvassers varied according to their persuasive ability or that endorser identity matters. Overall the results suggest a surprisingly muted role for idiosyncratic features of prospective persuaders. ∗We thank members of the BC Citizens ’ Assembly and Fair Voting BC, especially Arjun Singh, Wendy Bergerud and Maxwell Anderson. We are also very grateful to members of the BC-STV campaign. The campaign manager, Susan Anderson-Behn, as well as Maggie Gilbert and David Gagnon provided invaluable input and cooperation. Thanks to the canvassers and enumerators involved in carrying out the project. We thank our research project managers, Stewart Prest and Pierce O Reilly, for handling the coordination of enumerators. Don Green and David Epstein gave thoughtful comments on the project design and John Bullock and Jon Eguia provided helpful comments on our analysis. We thank seminar participants at the
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it