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The Loudspeaker and the Little Man: Mass Media and Democratic Participation in the Federal Theatre Project’s <i>One-Third of a Nation</i>

2020· article· en· W4231639837 on OpenAlexaff
Jordana Cox

Bibliographic record

VenueJournal for the History of Rhetoric · 2020
Typearticle
Languageen
FieldArts and Humanities
TopicRhetoric and Communication Studies
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsNewspaperDemocracyMedia studiesNarrativeRhetoricSociologyMass mediaPoliticsPolitical scienceLawLiteratureArtLinguistics

Abstract

fetched live from OpenAlex

ABSTRACT In a 1938 hearing with the House Un-American Activities Committee (HUAC), director Hallie Flanagan claimed that the U.S. Federal Theatre Project (FTP) (1935-9), which she led, disseminated “propaganda for democracy.” In this paper, I explore the possibilities of propaganda for democracy to describe FTP rhetoric. My focus is a body of work that greatly concerned HUAC. Living Newspapers were full-length, documentary plays about hot-button issues. At the New York Living Newspaper Unit (NYLNU), where journalists and theatre-makers worked side-by-side, Living Newspapers wove original reporting into dramatic narratives. Using theatrical conventions, they reimagined democratic communication for a mass society. My case study is the NYLNU’s most popular Living Newspaper. “One-Third of a Nation” was most explicitly about the lack of affordable housing across the U.S. It was also, however, about how to participate in democratic life. More specifically, it was about how to look at, listen to, and speak up about social issues. One-Third of a Nation addressed these themes by way of two recurring characters: a resounding Loudspeaker, and a plucky Little Man who represented the “average” citizen. Their interplay modeled propaganda for democracy as a dynamic relationship between responsive mass media and self-conscious public speech.

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 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: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.869
Threshold uncertainty score0.707

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.105
GPT teacher head0.273
Teacher spread0.169 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
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
Published2020
Admission routes1
Has abstractyes

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