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Record W4415312797 · doi:10.23977/aetp.2025.090517

An Exploration of Vocational English Teaching from the Perspective of Eco-Translatology

2025· article· W4415312797 on OpenAlexvenueno aff

Bibliographic record

VenueAdvances in Educational Technology and Psychology · 2025
Typearticle
Language
FieldEnvironmental Science
TopicEducational Reforms and Innovations
Canadian institutionsnot available
FundersDivision of Undergraduate EducationEducation Department of Hainan Province
KeywordsVocational educationPerspective (graphical)Process (computing)Work (physics)Focus (optics)Core (optical fiber)

Abstract

fetched live from OpenAlex

With the deepening of globalization, cultivating inter-disciplinary talents with excellent vocational English communication skills has become an urgent demand of the times. However, there is a prevalent issue of "ecological imbalance" in current vocational English teaching practice in China, which is disconnected from real vocational scenarios. To address this dilemma, this paper introduces the Eco-translatology theory proposed by Professor Hu Gengshen as a new research perspective. This theory regards language activities as a dynamic process in which the translator adapts to a specific ecological environment and makes choices, and its "three-dimensional transformation" methodology provides profound insights for the reform of vocational English teaching. This paper first explains the core essence of Eco-translatology, and then conducts an in-depth analysis of the "ecological imbalance" problem existing in current teaching. On this basis, the paper will focus on constructing a vocational English teaching model with "three-dimensional transformation" as the core and typical work tasks as the carrier, aiming to provide useful theoretical reference and practical guidance for improving the effectiveness of vocational English teaching and cultivating international talents that meet the needs of the times.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.223
Threshold uncertainty score0.849

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.002
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.001
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 head0.015
GPT teacher head0.370
Teacher spread0.356 · 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 designTheoretical or conceptual
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
Published2025
Admission routes1
Has abstractyes

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