Bibliographic record
Abstract
Dans le douzième film de Kitano Takeshi, Takeshis’ (2005), le célèbre chanteur/acteur queer Miwa Akihiro qualifie de monstre la grande vedette de télévision et de cinéma Beat Takeshi, alter ego du cinéaste Kitano Takeshi. Comment doit-on interpréter cette référence au monstre, tant au niveau diégétique que non-diégétique ? Si Beat Takeshi ou Kitano Takeshi sont qualifiés de monstre dans ce film, de quel genre de monstre s’agit-il, et quelle en est la signification ? L’origine latine du terme « monstre », « monstrum », signifie « symptôme » ou « avertissement ». Quels symptômes ou avertissements pouvons-nous détecter dans Takeshis’ ? Procédant d’une analyse approfondie des détails visuels de Takeshis’, et plus particulièrement de la représentation du désordre schizophrène de Beat Takeshi/Kitano Takeshi, cet article propose une interprétation faisant de Takeshis’ un symptôme de l’identité multiple des contextes culturels et technologiques de l’ère numérique.
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.
How this classification was reachedexpand
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 teacher head, 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".