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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 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)0.008
metaresearch head score (Gemma)0.009
Version: metacan-v3-hybrid-931329e0061cValidation 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: Empirical
Teacher disagreement score0.027
Threshold uncertainty score0.045

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0080.009
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0270.023
Scholarly communication0.0140.006
Open science0.0010.011
Research integrity0.0030.007
Insufficient payload (model declined to judge)0.0080.001

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 head0.105
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 source (direct Gemma or distilled Codex), 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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