The Temperature at which Partisanship Spreads: A Genre Analysis of American Partisan Political Documentaries
Bibliographic record
Abstract
This paper presents a genre analysis of a selection of American partisan political documentaries released between 2004 and 2012, including Michael Moore’s Fahrenheit 9/11 and Dinesh D’Souza’s 2016: Obama’s America. Since the release of Moore’s film, the genre has achieved commercial success despite a relative polarization between the critical responses towards liberal films and conservative ones. This essay determines that both liberal and conservative documentaries assign heroic and trustworthy roles to individual reporters and selections of interviewees, while leaders of the opposing party are villainized exclusively through selected archival video clips. However, while liberal documentaries are more prone to demonstrate a range of emotional appeals, conservative documentaries are more likely to rely on fear-mongering and angry aesthetics to persuade viewers.
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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 | 0.002 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
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".