Bibliographic record
Abstract
Televised debates are an extremely important election campaign medium for several reasons. Many voters watch debates; they also learn about debates from news and/or from discussions with other voters. Furthermore, televised campaign debates are becoming more common over time. Every American general election presidential campaign since 1976 has featured at least one debate (the first American general election debate occurred in 1960, when Nixon and Kennedy squared off). Recent years have seen dozens of US presidential primary debates (the first presidential primary debate was broadcast on radio in 1948). Debates for other elected offices—US senate or congress, governor, and mayor—are becoming more common. Other countries, including Australia, Britain, Canada, Germany, Iran, Italy, Poland, South Korea, Spain, Taiwan, and Ukraine, have also held debates among political leaders in recent years. Debates are important because they allow voters to directly compare leading candidates, and the face-to-face confrontation of debates can generate a direct clash of ideas, helping voters learn more. Although candidates prepare for debates, candidates usually are not allowed to bring prepared notes to the debate. This can mean that unexpected questions or comments from an opponent may provoke more candid answers than found in heavily scripted spots or speeches. Debates provide extended exposure to candidates. Debates are longer than most other campaign messages (usually one to two hours long), and some campaigns feature more than one debate. Finally, televised debates have substantial effects on viewers, as elaborated below. It should be noted that debates usually feature a question and answer format, with candidates alternating answers to each. Some debates permit candidates to make opening statements and/or closing remarks not prompted by questions. Without question, debates merit scholarly attention.
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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.187 | 0.036 |
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".