L’écrit à l’écran : écriture, texte et lisibilité dans la transcréation québécoise
Bibliographic record
Abstract
From the first movies to talkies, films have never been able to completely emancipate from language, the main way of communicating a story, and thus to ignore the written word. It can however be surprising that the written word hasn’t completely disappeared, even retaining an important role in talking cinema, since actors can now express themselves verbally and the audience can gather, thanks to the audio, all the information required to understand. There still remain some traces of writing on the screen, which Chion (2013) distinguishes as being “diegetic” or “non-diegetic.” Several types of writing, distinguished by their relation to the film’s diegesis, require the reviewer to give them particular attention since, even though their presence can sometimes be ignored, such presence is not natural in the “audio-visual.” Focus on the written word is even more important for those who study film adaptations or “transcreations”, since the diegesis is, as the first witness of the “story” (Gaudreault, 1988), conveyed in writing only. The first question will thus be to analyze the characteristics of the endurance of hypotexts via the written word in transcreations and to evaluate its presence in relation to films d’auteur. The second question, which goes beyond the typology proposed by Chion, will be to see if the written word can be limited by these two poles (diegetic and extradiegetic) or if their boundaries are porous, which would imply a reflexive and metafictional dialogue in many cases.
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 | 0.007 | 0.011 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.011 | 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".