Past Perfect(ed): Future Nostalgia and the Fight Against Trump’s America in Netflix’s Hollywood
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The election of Donald Trump in 2016 precipitated a crisis in national identity amongst liberal Americans leading to the political mobilization of grassroots activists, liberal media, and minority groups as a bulwark against the perception of a reassertion of intolerant conservatism. This article will examine the shared trauma of this historical moment through utilizing the Meaning Maintenance Model as a means to frame why this trauma was felt so deeply and collectively, but also to understand the conjunctions of resistance through direct action, media representations and nostalgia. Through the series Hollywood (2020) future nostalgia will be viewed as a tool by which present day resistance can be galvanized by presenting a fictional portrayal of post-war Hollywood as an era in which progressives fought for equality, exhibited intersectional allyship and potentially changed the social fabric of contemporary America, leading to a country in which Trump would have been unelectable and many ongoing battles for equality would have been won generations ago.
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Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it