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Record W7161328019 · doi:10.24310/redit.2008.i1.1900

Getting more than you paid for? Consideration in integrating free and low-cost technologies into translators training programs

2016· article· W7161328019 on OpenAlex
Lynne Bowker, Cheryl McBride, Elizabeth Marshman

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

Venueredit - Revista Electrónica de Didáctica de la Traducción y la Interpretación · 2016
Typearticle
Language
FieldArts and Humanities
TopicTranslation Studies and Practices
Canadian institutionsUniversity of OttawaUniversité Laval
Fundersnot available
KeywordsTraining (meteorology)SoftwareMachine translationEmerging technologiesLanguage industryTranslation (biology)

Abstract

fetched live from OpenAlex

Translation technologies are now an integral part of most translator training programs, and recently, a number of free and low-cost translation tools have begun to appear on the market. Because translator training programs typically have limited budgets, such software has great appeal. However, before adopting these tools, trainers must consider a range of questions, including practical issues, such as laboratory management and language considerations, as well as more pedagogically-oriented questions, including academic priorities, market needs, and possibilities for a wider integration of technologies into translation programs. This paper will explore such questions, and will introduce the Collection of Electronic Resources in Translation Technologies (CERTT) Project, discussing ways in which CERTT could potentially help to maximize the benefits of incorporating free and low-cost software into translator training.

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.

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)0.005
metaresearch head score (Gemma)0.004
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies, Scholarly communication
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.710
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.004
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0000.000
Science and technology studies0.0010.003
Scholarly communication0.0020.001
Open science0.0010.000
Research integrity0.0010.001
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 head0.019
GPT teacher head0.290
Teacher spread0.271 · 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