Bibliographic record
Abstract
Dans son livre sur Rabelais, Mikhaïl Bakhtine pose les principes d’une esthétique carnavalesque qui serait à l’origine de l’ensemble de la littérature « hétéroglossique », c’est-à-dire capable de brasser les discours peuplant un univers social. L’auteur établit un parallèle entre ces écrits et certaines fictions télévisuelles où se manifestent les traits caractéristiques du carnavalesque (la grossièreté, le brassage des genres, la conquête d’un point de vue discordant, l’impertinence envers les puissances), inspirés par le bref renversement social que constituent les carnavals au Moyen Âge. L’auteur propose de regarder un moment de l’histoire des séries télévisées à travers la perspective carnavalesque, capable selon lui de rendre compte du projet de ces séries. L’auteur étudie en détail la narration de deux séries carnavalesques :The Simpsons(1989-…) etBoston Legal(2004-2008). Dans la première, la narration fait d’une petite ville fictive, Springfield, un microcosme des attitudes et des stéréotypes américains aperçus à travers la loupe de la famille Simpson. Dans la seconde, des juristes obsédés, obsessionnels et pervers se confrontent aux douleurs d’une Amérique vacillante.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.007 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.013 | 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".