Bibliographic record
Abstract
This paper explores the modern metropolis as an ironically concrete metaphor for the collective memory and the mourning of cinema’s passing, as it—the “city”—is digitally constructed in two recent, auteur-directed, special effects-driven blockbuster films, Inception and Hugo. The modern city, and mass media, such as the cinema, as well as modes of mass transport, especially the train, all originate in the 19th century, but come into their own in the early 20th century in their address to a subject as the mobilised citizen-consumer who, as Anne Friedberg makes clear, is also always a viewer. Additionally, as Barbara Mennel has recently shown, the advent in Europe of trains and time zones, in their transformation of modern time and space, paved the way for cinema’s comparably cataclysmic impact upon modern subjectivity in its iconic reproduction of movement within illusory 3D space. Both films, thus, in their different ways employ cinematic remediation as a form of cultural memory whose nostalgia for cinema’s past is rendered with the latest digital effects, hidden in plain sight in the form of subjective memories (as flashback) and dreams. While a version of this reading has been advanced before (at least for Hugo), this paper goes further by connecting each film’s status as remediated dream-memory to its respective dependence upon the city as a post-cinematic three-dimensional framework within which locative and locomotive desires alike determine a subject whose psyche is indistinguishable from the cityscape that surrounds him.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.016 |
| Scholarly communication | 0.012 | 0.011 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| 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".