Past Perfect(ed): Future Nostalgia and the Fight Against Trump’s America in Netflix’s Hollywood
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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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.009 | 0.006 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.006 | 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".