Camera Movement, Reading, and Coloniality in Ichikawa Jun’s Film, Tony Takitani
Bibliographic record
Abstract
The function of film grammar in the creation of narrative cinema is a central one when considering the realities of cinema as a global art. Since its birth from a confluence of European scientific and aesthetic principles, cinema has become a ubiquitous art form, but together with this growth has come the spread of those very principles from which cinema sprang. As an example, camera movement in Japanese film typically follows a grammatical pattern to privilege left-to-right, chronological movement as set by western cinema. That is, the camera will introduce information as a visual analogue to the process of reading a written, western text, with the lens operating very much as an eye in its trajectory across the ‘page’ of the screen. Building on work by Jean-Louis Baudry, Brian O’Leary and Jean Louis Comolli, this paper demonstrates this feature of Japanese cinema, using Ichikawa Jun’s 2004 film, Tony Takitani, as a case study. Through a close reading of the film and its pattern of movement, this paper proposes that we may discern a symptom of the persistent inscription of coloniality imposed in and through cinema—the movement of the camera parodies reading but also accepts as natural an ‘unnatural’, western pattern of movement. The act of adaptation, too, both anticipates and supports the conception of cinema as reading-parody, with Murakami Haruki’s short story “Tony Takitani” operating as a meaningful substratum to the process of vision-as-coloniality.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.006 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| 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".