The nostalgic remediation of cinema in Hugo and Paprika
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
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.
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.
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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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