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Record W2594312546 · doi:10.21083/nrsc.v0i10.3708

La scène de mentorat – (Se) raconter la création littéraire en plein travail

2017· article· fr· W2594312546 on OpenAlexvenueno aff
Johanne Mohs, Marie Caffari

Bibliographic record

VenueNouvelle Revue Synergies Canada · 2017
Typearticle
Languagefr
FieldSocial Sciences
TopicWriting and Handwriting Education
Canadian institutionsnot available
Fundersnot available
KeywordsHumanitiesArtAuteur theoryPhilosophyArt history

Abstract

fetched live from OpenAlex

Résumé Dans les formations en écriture créative et dans les maisons d’édition, le travail littéraire des auteur-e-s est souvent accompagné par un-e autre auteur-e ou lecteur/-trice professionnel-le (chargé-e de supervision, mentor, éditeur/trice), intervenant dans le processus d’écriture. L’article analyse les échanges de quatre duos de mentorat ainsi que leur impact sur le processus d’écriture. En référence à la « scène d’écriture », l’article décrit cette pratique littéraire ouverte par la présence d’un-e autre en tant que « scène de mentorat », dans laquelle le texte littéraire s’élabore au cours d’un dialogue continu. La scène de mentorat est aussi envisagée comme un dispositif performatif, dans lequel le texte a la fonction de script du dialogue. Quant à l’interaction entre texte, auteur-e et mentor, elle peut être comprise en tant qu’une chaîne de feedbacks. AbstractIn creative writing courses and in literary publishing situations, authors contribute to the writing of their colleagues, as editors, supervisors or ‘mentors’. Literary works in progress are thus critically discussed and reviewed within an ongoing dialogue. The literary text is at the centre of this setting, in which the writing scene is not reserved to the author alone, but opened by the presence of another and can thus be considered as a ‘mentoring scene’. The article analyses the dialogues of four ‘mentoring duos’, evaluating their impact on the writing processes. It considers this specific writing scene as potentially performative, whereby the text acts as the script of the dialogue and finally considers the specific interaction between text, author, mentor/supervisor as a ‘feedback series’.

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)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.639
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0020.001
Scholarly communication0.0010.000
Open science0.0010.000
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 head0.017
GPT teacher head0.287
Teacher spread0.270 · 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.

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

Citations1
Published2017
Admission routes1
Has abstractyes

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