Scale Matters: Addressing the Limited Robustness of Findings on Negative Advertising
Bibliographic record
Abstract
Abstract Negative campaign advertising is a major component of the electoral landscape, and has received much attention in the literature. In many studies, political scientists have tried to explain why some campaign ads contain more negative messages than others and to identify the determinants of this form of campaign behavior. In recent years, a number of studies have acknowledged the differences between alternative measures of negativity, but, in most cases, it is assumed that since these measures are highly correlated, they are unidimensional and essentially interchangeable. In this article, we argue that much of the debate in the literature over negative campaigning is a result of inadequate operationalizations of negativity. Although debates over negativity have often been framed in conceptual terms, there is a methodological explanation for why they persist We begin our analysis by constructing reliable scales of negativity, and model them with salient predictors reported in the literature as significantly associated with campaign attacks. Our findings show that scaling does matter, and while some of the explanatory variables are robust predictors of negativity, most of them are not.
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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 | 0.199 | 0.623 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.006 |
| Bibliometrics | 0.006 | 0.009 |
| Science and technology studies | 0.004 | 0.013 |
| Scholarly communication | 0.008 | 0.011 |
| Open science | 0.007 | 0.008 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.012 | 0.002 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".