Bibliographic record
Abstract
At first glance, David Yates’s 2016 fantasy film Fantastic Beasts and Where to Find Them appears to exemplify the nostalgic cinema that Fredric Jameson dismisses as postmodern pastiche that merely imitates the past through superficial details such as setting and costumes. Set in New York in 1926, Fantastic Beasts evokes the elegance and allure of the Roaring Twenties. The film invokes an idealized past, re-presented through the lens of “magic” as a place where all social, racial, and gendered differences have been erased. Fantastic Beasts articulates a powerful cultural yearning for an idealized bygone era while superimposing contemporary concerns about liberty, equality, preservation, and ecology on the past. I argue, however, that Fantastic Beasts does not merely use nostalgia as a surface strategy to create cinematic allure. Embedded in the film’s self-reflexive structure is a deeper analysis of nostalgia itself, both in its reflective postmodern mode and in the form of a politically and religiously charged restorative nostalgia.
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 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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.007 | 0.017 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 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".