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LE MARCHE DE TRADUCTION AU CANADA ET AU NIGERIA :

2016· article· fr· W2611631823 on OpenAlexaffabout
Olusegun Tope Afolabi

Bibliographic record

VenueBelas Infiéis · 2016
Typearticle
Languagefr
FieldArts and Humanities
TopicTranslation Studies and Practices
Canadian institutionsUniversité Laval
Fundersnot available
KeywordsHumanitiesPolitical sciencePhilosophy

Abstract

fetched live from OpenAlex

À l’ère actuelle qui est sans aucun doute marquée par la mondialisation, il n’est plus à démontrer que notre monde devient de plus en plus un village planétaire où tout se rapproche en rien de temps; et d’où la nécessite de faciliter la communication et surtout la compréhension entre les peuples et les pays. Partant de ce constat, on sera en ordre de rappeler que la traduction occupe donc une place importante dans la société moderne. Cependant, force est de constater que l’activité traductionnelle ne suit pas le même rythme d’un pays à l’autre et cela est dû à bon nombre de facteurs. Dans la présente étude, notre objectif premier est de démontrer les différents niveaux auxquels se situe le marché de traduction entre les deux pays qui constituent notre étude de cas, en l’occurrence le Canada et le Nigéria. Le but ultime de l’étude, qui se veut à la fois analytique, comparative, informative et correctionnelle, est de juxtaposer les deux contextes canadien et nigérian afin de voir comment l’un peut tirer profit de l’expérience de l’autre, pour ainsi contribuer au développement de la traduction dans le monde.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.917
Threshold uncertainty score0.999

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 studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.042
GPT teacher head0.263
Teacher spread0.221 · 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

Citations0
Published2016
Admission routes2
Has abstractyes

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