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Record W1991489456 · doi:10.1080/10584609.2010.540305

Stimulating or Reinforcing Political Interest: Using Panel Data to Examine Reciprocal Effects Between News Media and Political Interest

2011· article· en· W1991489456 on OpenAlex

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

VenuePolitical Communication · 2011
Typearticle
Languageen
FieldSocial Sciences
TopicSocial Media and Politics
Canadian institutionsMacEwan University
Fundersnot available
KeywordsPoliticsPolitical communicationNews mediaPanel dataPolitical scienceDiversity (politics)ReciprocalVoting behaviorPublic relationsVotingEconomicsLawEconometrics

Abstract

fetched live from OpenAlex

Is the news media merely a tool for those already interested in politics, or can the news media stimulate interest in politics? While the news media likely serve both functions, little research has examined these dual functions and how television, print, and online news media differ in their performance of these functions. I use simultaneous equation modeling of 3-wave panel data from the American National Election Study (2008–2009) to examine the roles of different media in both stimulating and reinforcing political interest. The findings demonstrate that television news is a tool for those with prior interest in politics, more than a mechanism to influence levels of political interest. In contrast, online and print news can stimulate political interest to a greater degree than these media serve those with prior political interest. These differing relationships to political interest are explained in terms of the effort and attention required to use these news sources, their information-sharing capabilities, and their diversity of content.

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)0.020
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Meta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.414
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.020
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0000.001
Open science0.0010.002
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 head0.554
GPT teacher head0.453
Teacher spread0.101 · 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