Bibliographic record
Abstract
Cet article analyse la capacité à durer de trois des quatre séries créées par Aaron Sorkin, Sports Night, Studio 60 on the Sunset Strip et The Newsroom, qui ont pour sujet les coulisses de la télévision. En optant pour un propos avant tout descriptif (faire découvrir les dessous et surtout les contraintes d’un programme télévisé), elles se privent d’emblée d’un suspense à long terme au profit d’une tension à courte portée (les personnages parviendront-ils à boucler leur show à temps ?) On étudie d’abord les trois pilotes programmatiques qui amorcent un même arc narratif (la tension entre l’ambition d’excellence et les impératifs économiques qui entravent les personnages dans l’accomplissement d’une émission de qualité), puis la manière dont chacune de ces séries organise la suite, ralentissant ou préparant sa chute. Ce faisant, on se propose d’examiner les rapports que ces séries entretiennent avec le feuilleton, le cycle, ainsi que les stratégies narratives qu’elles déploient, telles que l’insertion d’intrigues à suspense, pour se relancer et finir sans en finir.
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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.006 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.035 | 0.007 |
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".