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Record W1989688217 · doi:10.7202/1008340ar

Knowledge Structure and Training of Translation Teachers: An Exploratory Study of Doctoral Programmes of Translation Studies in Hong Kong

2012· article· en· W1989688217 on OpenAlexvenueno aff
Defeng Li, Chunling Zhang

Bibliographic record

VenueMeta Journal des traducteurs · 2012
Typearticle
Languageen
FieldSocial Sciences
TopicEducator Training and Historical Pedagogy
Canadian institutionsnot available
Fundersnot available
KeywordsContext (archaeology)CurriculumPerspective (graphical)Training (meteorology)Exploratory researchTranslation studiesKnowledge translationMedical educationQuality (philosophy)Qualitative researchPedagogyMathematics educationPsychologySociologyMedicineComputer scienceSocial scienceLinguisticsKnowledge managementHistoryGeography

Abstract

fetched live from OpenAlex

A rapid expansion of translation programmes at all levels the world over in recent years has heightened the demand for quality translation teachers. However, little research has been carried out to date on translation teacher training – M.Phil./Ph.D. programmes of translation studies in tertiary institutions despite a plethora of studies on general translation teaching. The present study, intended as an initial attempt to address the issue, approaches the topic from a teacher education perspective and looks critically at the knowledge structure of translation teachers. On this basis, the present article reports on a qualitative case study conducted in 2004-2007 on the curriculums and particularly the needs and experiences of the research students at the M.Phil./Ph.D. programmes of translation studies in Hong Kong. Although the study was conducted in the context of Hong Kong, the findings and implications may apply to other translation teacher training programmes the world over.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.210
Threshold uncertainty score0.485

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.001
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 head0.446
GPT teacher head0.451
Teacher spread0.006 · 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 designQualitative
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

Citations14
Published2012
Admission routes1
Has abstractyes

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