The Loudspeaker and the Little Man: Mass Media and Democratic Participation in the Federal Theatre Project’s <i>One-Third of a Nation</i>
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.008 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.027 | 0.023 |
| Scholarly communication | 0.014 | 0.006 |
| Open science | 0.001 | 0.011 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".