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Record W2311078436 · doi:10.1177/0093650215608239

Protests on the Front Page: Media Salience, Institutional Dynamics, and Coverage of Collective Action in the <i>New York Times</i> , 1960-1995

2015· article· en· W2311078436 on OpenAlexfundno aff
Patrick Rafail, Edward T. Walker, John D. McCarthy

Bibliographic record

VenueCommunication Research · 2015
Typearticle
Languageen
FieldSocial Sciences
TopicMedia Influence and Politics
Canadian institutionsnot available
FundersSocial Sciences and Humanities Research Council of CanadaNational Science Foundation
KeywordsSalience (neuroscience)Media coverageCollective actionPublishingMass mediaSocial mediaMedia studiesNews mediaPolitical scienceFront (military)Action (physics)Media eventSociologyAdvertisingLawPsychologyPoliticsGeographyBusiness

Abstract

fetched live from OpenAlex

Past research has illuminated consistent patterns in the type of protests that receive media attention. Still, we know relatively little about the differential prominence editors assign to events deemed worthy of coverage. We argue that while media routines shape whether events are covered, mass media organizations, social institutions, and systemic changes are important factors in determinations of prominence. To examine patterns of prominence, this study analyzes the factors influencing page placement patterns of protests covered in the New York Times, 1960-1995. We find that (1) protests are less likely to appear prominently over time, but this effect is conditioned by the paper’s editorial and publishing regime; (2) regime effects were especially consequential for civil rights and peace protests; (3) effects of event size and violence weakened over time; and (4) events embedded within larger cycles of protest coverage during less constricted news cycles were more likely to be featured prominently.

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)0.001
metaresearch head score (Gemma)0.012
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 score0.027
Threshold uncertainty score0.053

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.012
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0030.003
Science and technology studies0.0010.001
Scholarly communication0.0020.001
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0040.000

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 head0.250
GPT teacher head0.437
Teacher spread0.187 · 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

Citations17
Published2015
Admission routes1
Has abstractyes

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