Boost or backlash? The electoral impact of the 1993 Chrétien attack ad
Bibliographic record
Abstract
Existing studies have not clearly established the effectiveness of negative advertising as a campaign strategy. This thesis examines whether attack ads have a causal effect on political parties’ electoral support; whether their impact decays over time; and whether the impact of attack ads differs depending on voters’ political awareness and ambivalence. These questions are addressed by exploiting a natural experiment generated by the airing of an infamous attack ad – the face ad – during the 1993 Canadian federal election campaign. Given that the timing of the survey interviews is as-if random, the release of the face ad provides a unique opportunity to assess the causal impact of negative ads on vote intentions. The findings indicate that: (1) attack ads lead to a substantial decrease in electoral support for sponsoring parties; (2) attack ads lead to a substantial increase in support for targeted parties; (3) the effects of attack ads do not decay; and (4) the probability of voting for the sponsors of attack ads after their initial broadcast is similar regardless of voters’ political awareness and whether or not they have strong predispositions, but the probability of voting for the targets of attack ads is significantly higher when voters are ambivalent.Keywords: Negative advertising, natural experiment, party support, vote intentions, attack ads
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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 | 0.007 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".