Bibliographic record
Abstract
Dis/enclosures and Im/mobilities 1At the beginning of Cary Joji Fukunaga's 2009 film Sin Nombre, which tells the story of a group of Hondurans attempting to cross into the United States via Mexico by riding aboard a fictional version of the freight train known as 'La Bestia' or 'El tren de la muerte,' we encounter a scene of im/mobility. 1 In the film's first shot, we see a path in an autumn-coloured forest leading away from the camera until it disappears on the brightly lit horizon, with the camera slowly tracking forward as if following the path.In the second shot, we see a young man sitting motionless on a chair in a small and dark room, staring towards the camera, which slowly moves towards him.In the third shot, we see the path again, establishing it as the object of the young man's gaze, before the spatial relations are clarified in the fourth shot, which shows him from behind, staring at what reveals itself to be a poster in the adjoining room, now framed by the thick stone walls that separate the two rooms. Cinematic Im/mobilities in the Planetary NowTranstext(e)s Transcultures 跨文本跨文化, 17 | 2022
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.012 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.000 | 0.005 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.024 | 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".