Bibliographic record
Abstract
Is a schizoanalysis of cinema possible? This question arises from the observation that there is no apparent continuity between Deleuze’s two-volume collaboration with Guattari, Anti-Oedipus and A Thousand Plateaus , and the books he wrote afterwards, Cinema 1 and Cinema 2 . It is also prompted by the observation that Anti-Oedipus and A Thousand Plateaus seem to rely a great deal on cinema in order to develop and exemplify the many new concepts these books introduce. This paper highlights three such instances in their work. The fact is, Deleuze and Guattari claim that the core schizoanalytic concepts of the body without organs, the abstract machine and assemblage can account for “all things”; as such, these concepts must account for cinema too. It is the sheer expansiveness of these concepts that makes them attractive to cinema studies. Not only that, they promise a way of engaging with cinema that isn’t reliant on the fictions of identification, recognition and fantasy. In this sense we are permitted to assume that to some degree Cinema 1 and Cinema 2 are already schizoanalytic, albeit in ways we have yet to properly understand. The author makes a direct link between cinema and schizoanalysis by highlighting the significance of delirium to both. This paper argues that the royal road to a schizoanalysis of cinema is via delirium rather than dream or fantasy. It goes on to show how Deleuze and Guattari’s formalisation of delirium as a “regime of signs” can be used to inaugurate a new kind of semiology of cinema.
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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 | 0.005 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.008 | 0.058 |
| Scholarly communication | 0.014 | 0.021 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.002 | 0.007 |
| Insufficient payload (model declined to judge) | 0.004 | 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".