Going negative when spoiled for choice? Destabilizing and boomerang effects of negative political messaging in multiparty systems with multimember districts
Bibliographic record
Abstract
Classical electoral behaviour theories have associated possible benefits of negative campaigning with two-party plurality systems due to their zero-sum nature. Nevertheless, negative campaigning is a widely used electoral strategy outside of these contexts, despite scant evidence of its benefits for political parties and candidates who employ it. Our research question is simple – is negative campaign messaging effective for attackers in multiparty systems with multimember districts? Or does it create a ‘boomerang effect’ in this context, for which the producer of the message faces a backlash? Multiparty systems with multimember districts should, according to the literature, be scenarios where the effects of negative campaigning are most complex if not unpredictable. This paper uses Facebook political messages to inform a survey experiment design that tests the effects of negative political messaging on voters. We employ this survey in Ireland, which uses the single transferable vote, an electoral system which magnifies outcome uncertainty for attackers. Our results suggest that negative messaging in this context produces both the intended effect and a boomerang effect for the sponsor of the message. These countervailing results suggest a net null effect for the efficacy of negative messaging.
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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 | 0.006 | 0.039 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.016 | 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".