Bibliographic record
Abstract
Les jeux modernes pour ordinateurs ont tendance `a apporter les structures narratives complexes, en affirmant un jeu `a la fois entendu et int´eressant. Les structures narratives sont importantes non seulement dans le jeu d'aventure et le jeu de rˆole, mais aussi dans le jeu de tir `a la premi`ere personne et le jeu de strat´egie. Malgr´e tout, beaucoup de structures narratives poss`edent des d´efauts qui r´eduisent la qualit´e des jeux, ainsi c'est important de d´evelopper la technique de analyse ou de v´erification. Malheureusement, des tentatives de v´erification formelle sont limit´ees, et dans la pratique effectu´ee avant tout par l'inspection manuelle et l'essai du jeu. Notre recherche est bas´ee sur une structure applicative nomm´ee Programmable Narrative Flow Graph (PNFG) (Plan de Flux Narratif Programmable) qui offre un langage de haut niveau `a repr´esenter des structures narratives. En tant que premier pas vers la v´erification plue profonde, notre approche est `a appliquer l'analyse formelle de haut niveau sur les structures narratives afin de trouver leurs winning paths (chemins de la victoire). Nous prolongeons la structure applicative PNFG par l'´elaboration d'un module d'analyse de flux de donn´ees g´en´eriques, mais aussi mettons en oeuvre plusieurs analyseurs `a recueillir des donn´ees de haut niveau sur les comportements narratifs. Pour am´eliorer la performance de notre approche, nous avons ´egalement conc¸u plusieurs optimisations qui r´eduisent la taille de l'espace de recherche. Non toutes les diff´erentes optimisations sont efficaces sur les structures narratives, mais notre strat´egie finale et totalement optimis´ee$
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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 | 0.002 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".