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Record W7111878409

Context, Field and Landscape of Audiovisual Translation in the Arab World

2020· article· en· W7111878409 on OpenAlexaboutno aff

Bibliographic record

VenueESSACHESS/Essachess · 2020
Typearticle
Languageen
FieldArts and Humanities
TopicTranslation Studies and Practices
Canadian institutionsnot available
Fundersnot available
KeywordsState (computer science)Quarter (Canadian coin)Field (mathematics)PoliticsTranslation studiesArabic
DOInot available

Abstract

fetched live from OpenAlex

Translation, as a cultural mediation, builds bridges between the Arab world and the outside world, particularly the west and continues to occupy a pivotal place in Arab society. Over the past two centuries, and since the establishment of the school of translation in Cairo in 1835, translation has been viewed as a vehicle of Nahda (progress) and Tanweer (enlightenment). Over the past two decades, however, translation in the Arab world has been radically transformed both at the practice and policy levels. The turn of the new millennium has brought about changes that have shaken the state of affairs and challenged old thinking and the ways of doing things. First, digital technology has changed the way things are done from work, play and study to the ways we socialise, shop and entertain ourselves. Second, a report on human development in the Arab world published in 2002 by the United Nations Development Programme (UNDP), revealed the unhealthy state of translation in most Arab countries. The paper examines the state of audiovisual study in Arabic and invites scholars to focus a lot more on their own local environment. It argues that a quarter of a century after the conference that launched the concept of AVT in Europe in 1995, the time has come for Arab academia to start developing its (own) theoretical frameworks for the localisation of audiovisual translation studies with the view of making translation studies not only relevant to society but also to play the role it was envisaged two centuries earlier.

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: none
Teacher disagreement score0.918
Threshold uncertainty score0.672

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 communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.105
GPT teacher head0.284
Teacher spread0.179 · 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
Published2020
Admission routes1
Has abstractyes

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