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Record W3169343923

The Impact of Televised Debates in the U.S. And Germany: How Important is Emotion in Modern Campaigns

2013· article· en· W3169343923 on OpenAlexaboutno aff
Haseeb Mahmud, Peter R. Schrott, David J. Lanoue

Bibliographic record

VenueSSRN Electronic Journal · 2013
Typearticle
Languageen
FieldSocial Sciences
TopicSocial Media and Politics
Canadian institutionsnot available
Fundersnot available
KeywordsPersuasionPoliticsPresidential systemPolitical sciencePresidential electionPolitical economyPositive economicsMedia studiesSociologySocial psychologyLawPsychologyEconomics
DOInot available

Abstract

fetched live from OpenAlex

Beginning in the middle of the last century, presidential debates in the U.S. have attracted significant interest from academics as well as the general public. But the United States is not the only country that televises debates during their general election campaigns. Germany, for example, saw its first debate in 1972, and with a few exceptions, has had at least one debate in every national election since. There is already a considerable amount of literature suggesting that debate performances have the ability to influence candidate evaluations. However, researchers generally assert that the most common impact of political debates is to reinforce partisan preferences. Scholars have shown that pre-debate supporters and strong partisans are highly likely to believe that their preferred candidate is also the debate “winner”. But reinforcement is not the only possible effect of presidential debates on their audience. Another potential effect is known as activation. Many voters hold only weak attachments to the political parties. Further, they may not pay close attention during the political campaign, except for a few highly publicized events. For such weak partisans, debates may enhance or strengthen their tentative attachment to the party and its leader. Thus, they do more than simply reinforce preferences—they activate loyalties and turn indifferent citizens into committed supporters. A final potential impact of debates is the so-called persuasion effect, in which voters are actually drawn to a candidate they would otherwise not have voted for as a result of watching the debates. Early research, particularly on American presidential debates, cast significant doubt on the ability of these events to influence voting decisions. More recent evidence, however, has shown that, under certain circumstances, debates can, in fact, alter election results. Moreover, such an impact has been documented in several countries, including, among others, the U.S., Germany, and Canada. What is largely missing from the debate literature is comparative and cross-national research. Our paper is an attempt to fill that gap. In our manuscript, we will analyze two countries that hold debates regularly, the U.S. and Germany. Using American National Election Survey (ANES) panel data, we will document changes in voters' attitudes toward the candidates, and establish a strong connection between voters' changed opinions and debate watching. We will then conduct similar analyses of German debates, using the German Longitudinal Election Survey, to compare and contrast debate effects between the two countries. We will argue that, in general, the impact of debates is independent of political systems, and that the effects of debates in Germany and the United States are surprisingly similar. Further, we will argue that these effects stem not just from the informational content of the debates, but more importantly from the emotional reactions voters have to the candidates, their rhetoric, and their personal presentations.

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 imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.006
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.010
Threshold uncertainty score0.021

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.002
Scholarly communication0.0040.002
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.000

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.

Opus teacher head0.012
GPT teacher head0.294
Teacher spread0.282 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2013
Admission routes1
Has abstractyes

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