Lâautoréflexion de lâénonciation filmique\ndu film Punch-Drunk Love
Bibliographic record
Abstract
Le film Punch-Drunk Love, réalisé en 2002 par Paul Thomas Anderson, présente\nune approche formelle hors du commun et expose, de manière flagrante, le travail\nénonciatif du film. L’énonciation filmique qui est sienne pousse le spectateur à se distancer\nde l’oeuvre et l’incite à se questionner sur la signification des codes mis en place. Ce dernier\nest également stimulé à remettre en question ses propres attentes ainsi qu’à s’interroger sur\nson plaisir spectatoriel. Nous proposons, dans le cadre de ce mémoire, de faire l’analyse de\nl’énonciation filmique du film à travers l’approche abstraite de Christian Metz. Grâce à sa\nthéorie, qui affiche la préséance du film sur l’auteur en matière de signification des codes,\nnous mettrons en évidence l’idée que les constructions énonciatives fortes reprennent à leur\ncompte l’histoire racontée par le film. L’énonciation réfléchit et redouble le film. Avec une\ntelle approche, le film expose son rapport fusionnel entre le fond et la forme.\nL’énonciation, vue sous cet oeil, devient l’alter ego du film.
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 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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.025 | 0.003 |
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".