Bibliographic record
Abstract
Abstract This article addresses the ways in which two recent works of digital cinema, Martin Scorsese’s Hugo (2011) and Satoshi Kon’s Paprika (2006) revive classical (photochemical) cinema through what is termed ‘nostalgic remediation’. Rather than seeing nostalgia as ironic, ahistorical pastiche, as in Fredric Jameson’s description of postmodern nostalgia films, this article asks: how can we understand nostalgia as part of our own lived, affective experience of film within today’s new media ecology? To answer this question, it draws on theories of post-celluloid adaptation and remediation to demonstrate the ambivalent relationships between historical and current media platforms seen in digital cinema. These ambivalences, it is argued, reflect the broader anxieties and aspirations that arise in times of technological and social transition, such as the changes brought about by the digitization of media at the turn of the twenty-first century. Hugo and Paprika perfectly illustrate the delicate tension of nostalgic remediation, which shifts between transcending celluloid cinema and longing for its return; between the recovery and loss of cinema’s historical memory; and between the concepts of old and new media themselves.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.004 | 0.010 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| 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".