Bibliographic record
Abstract
L’objectif de cet article est d’opérer une lecture au-dedans et au-dehors de la fiction mise en place par le cinéaste Brian Flemming dans son documentaire Nothing So Strange (GMD Studios, 2002), ainsi que dans les documents d’archive et les artéfacts web produits en son sillage. L’univers Nothing So Strange s’attache à décrire l’un des événements historiques les plus importants à n’avoir pas eu lieu au xx e siècle : l’assassinat du président de Microsoft Bill Gates. Nous tenterons de voir comment une grande oeuvre participative (au sens de Jenkins) s’est développée à partir du film de Flemming, témoignage hyperréaliste issu d’un univers parallèle ; comment cinéastes, spectateurs et internautes ont détourné la capacité documentaire de l’archive filmique et numérique afin de se réapproprier la figure historique du complot meurtrier et fournir à la fiction un grand déploiement tentaculaire. Nous interrogerons également les enjeux éthiques et esthétiques de cette stratégie, que Flemming lui-même qualifie de piratage de la réalité («reality-hacking»).
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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.009 | 0.008 |
| Scholarly communication | 0.012 | 0.006 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.022 | 0.004 |
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".