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Record W207278832

Talking Heads: How Broadcast Media Frame the Public Relations Industry.

2011· article· en· W207278832 on OpenAlexaboutno aff
Samara Rose Litvack

Bibliographic record

VenueDigital Commons - East Tennessee State University (East Tennessee State University) · 2011
Typearticle
Languageen
FieldSocial Sciences
TopicEducation, Sociology, Communication Studies
Canadian institutionsnot available
Fundersnot available
KeywordsFrame (networking)BusinessPublic relationsAdvertisingPolitical scienceMedia studiesTelecommunicationsComputer scienceSociology
DOInot available

Abstract

fetched live from OpenAlex

Researchers conducted a content analysis to measure framing of the public relations industry in 354 English language broadcast transcripts from the United States, Canada, and Australia from Sept. 1, 2009 to Aug. 31, 2010. The overall tone toward public relations was strongly negative. Mentions reflected one-way forms of communication and mentions of the pejorative term "PR" appeared more frequently than mentions of "public relations". The profession was almost always mentioned within the body of the broadcast, as opposed to the headline or the lead paragraph. Exploratory research showed 15 shows that included negative mentions 100% of the time. Additionally, 27 shows included zero positive mentions of either term. Of 251 speakers recorded during data analysis, 126 spoke of the industry negatively 100% of the time. American shows were most often negative. Stories about the public relations industry were most likely to reflect public relations as a two-way form of communication.

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)0.003
metaresearch head score (Gemma)0.019
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.028
Threshold uncertainty score0.055

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.019
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0030.002
Science and technology studies0.0030.002
Scholarly communication0.0040.004
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.001

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 head0.092
GPT teacher head0.266
Teacher spread0.174 · 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 designQualitative
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
Published2011
Admission routes1
Has abstractyes

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