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Record W3159291370 · doi:10.3138/cjfs-2019-0019

L’écrit à l’écran : écriture, texte et lisibilité dans la transcréation québécoise

2021· article· en· W3159291370 on OpenAlexaffvenue
Marie Pascal

Bibliographic record

VenueCanadian Journal of Film Studies · 2021
Typearticle
Languageen
FieldComputer Science
TopicCultural Insights and Digital Impacts
Canadian institutionsDalhousie University
Fundersnot available
KeywordsWitnessNarrativeLiteratureMovie theaterArtRelation (database)LinguisticsHistoryPhilosophyComputer science

Abstract

fetched live from OpenAlex

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.601
Threshold uncertainty score0.808

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.126
GPT teacher head0.309
Teacher spread0.183 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2021
Admission routes2
Has abstractyes

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