<em>Ryan</em>: Une rencontre dans l'espace figural de la souffrance
Bibliographic record
Abstract
Je me propose, dans cet article, de suivre le fil ténu — mais tenace — qui relie, dans une œuvre, les figures les unes aux autres. L’œuvre que j’entends parcourir de cette façon est le court métrage Ryan, de Chris Landreth : j’y cheminerai en passant d’un motif à l’autre (la blessure et l’agression), d’une figure à l’autre (figures du vide, de la disparition, de l’échec et de la mort), pour m’arrêter, enfin, sur ce que j’appelle l’espace figural de la souffrance : espace intersubjectif où la souffrance, ni directement perceptible, ni même pensable, ne peut être qu’éprouvée.AbstractIn this paper, my purpose is to follow the fine but strong thread that links, in an artwork, figures together. The artwork I will explore in that way is a short film from Chris Landreth, Ryan. From a pattern to another (injury and aggression), from a figure to another (figures of emptiness, disappearance, failure and death), my analysis will finally end on what I call a “figural space of suffering”: intersubjective space where suffering, neither perceptible to us, nor thinkable, may only be felt.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.017 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.014 | 0.002 |
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".