Attack Politics: Negativity in Presidential Campaigns since 1960. By Emmett H. Buell Jr. and Lee Sigelman. (Lawrence: University Press of Kansas, 2008. xii, 354 pp. $34.95, ISBN 978-0-7006-1561-2.)
Bibliographic record
Abstract
Considering that one version of hell has sinners reliving their worst moments again and again, in perpetuity, a chronicle of negative campaigning from 1960 to 2004 can be pretty depressing. Just as in a mudslide, once discrete rocks and dirt all blur together, a catalog of mudslinging quickly degenerates into a seemingly endless, often indistinguishable, barrage. The authors, both prominent political scientists, do not make the reading easier with a dry style and a clinical approach that is encyclopedic and exhausting. Still, having “extracted 17,124 campaign statements from 10,686 news items” published in the New York Times about the twelve presidential contests they studied, the authors offer interesting conclusions that defy conventional wisdom (p. 16). They argue that campaigns have not become more negative. They do not agree that Republicans are more negative than Democrats. They reject the claim that candidates dodge serious issues. And, having identified 1960 as the most negative campaign, they say John F. Kennedy just barely beats out Walter Mondale and George McGovern as the most negative major party campaigner in the modern era.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.007 | 0.009 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.011 | 0.005 |
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".