Animalités augmentées : leçons filmiques contemporaines
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Cet article cherche à étudier la manière dont les récits filmiques contemporains fabulent avec des animaux « augmentés », c’est-à-dire résultant d’une fabrication humaine volontaire. Nous cherchons d’abord à décrire ces formes animales contemporaines à partir de trois exemples : Okja (Bong Joon-ho, 2017), Jurassic World (Colin Trevorrow, 2015), et « Hated in the Nation » (James Hawes, 2016), puis nous nous penchons sur les fonctions qui leur sont attribuées. Cela nous conduit enfin à interroger la place laissée à une authentique existence animale dans ces fables, en nous appuyant sur deux exemples supplémentaires : Les Gardiens de la galaxie vol. 3 (James Gunn, 2023) et Eo (Jerzy Skolimowski, 2022).
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.011 | 0.008 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.012 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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 it