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Profiting From the “Trump Bump”

2019· book-chapter· en· W2947947665 on OpenAlex
Sergei A. Samoilenko, Andrey Miroshnichenko

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueAdvances in media, entertainment and the arts (AMEA) book series · 2019
Typebook-chapter
Languageen
FieldSocial Sciences
TopicSocial Media and Politics
Canadian institutionsYork University
Fundersnot available
KeywordsPoliticsAppropriationDeliberationPolitical communicationFraming (construction)Media ecologyPolitical scienceDemocracyScholarshipDominance (genetics)Media studiesPublic relationsPolitical economySociologyLawEpistemologyHistory

Abstract

fetched live from OpenAlex

This chapter contributes to scholarship in the fields of media ecology and political communication by investigating the effects of the Trump bump in media-driven democracy. Specifically, it explains how the media's obsession with Donald Trump allowed them to capitalize on his political brand, which in turn contributed to changing the tone of political discourse in the United States. The effects of mediatization, including click-bait framing, increased negativity, and person-centered media coverage, had a distinct impact on the behavior of political actors and the political system as a whole. The dominance of marketing logic in contemporary media democracies provides a compelling argument for critical investigation of brand appropriation in political communication and its impact on the state of democracy. This chapter advocates for the further investigation of the current media ecosystem in order to move toward a public deliberation model that would support enhanced media literacy and citizen engagement in public policy debates.

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.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.839
Threshold uncertainty score0.928

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0010.003
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.013
GPT teacher head0.265
Teacher spread0.253 · 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