Losing on all fronts: The effects of negative versus positive person‐based campaigns on implicit and explicit evaluations of political candidates
Bibliographic record
Abstract
The current research investigated the effects of negative as compared to positive person-based political campaigns on explicit and implicit evaluations of the involved candidates. Participants were presented with two political candidates and statements that one of them ostensibly said during the last political campaign. For half of the participants, the campaign included positive remarks about the source of the statement (positive campaign); for the remaining half, the campaign included negative remarks about the opponent (negative campaign). Afterwards, participants completed measures of explicit and implicit evaluations of both candidates. Results indicate that explicit evaluations of the source, but not the opponent, were less favourable after negative as compared to positive campaigns. In contrast, implicit evaluations were less favourable for both candidates after negative campaigns. The results are discussed in terms of associative and propositional processes, highlighting the importance of associative processes in political decision making.
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How this classification was reachedexpand
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 | 0.001 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".