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Record W3148079394 · doi:10.14746/ssllt.2021.11.1.4

Exploring learners’ understanding of technical vocabulary in Traditional Chinese Medicine

2021· article· en· W3148079394 on OpenAlex

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

VenueStudies in Second Language Learning and Teaching · 2021
Typearticle
Languageen
FieldPsychology
TopicSecond Language Acquisition and Learning
Canadian institutionsWestern University
Fundersnot available
KeywordsVocabularyWord AssociationDivergence (linguistics)PsychologyTraditional Chinese medicineWord (group theory)LinguisticsComputer scienceMathematics educationArtificial intelligenceAlternative medicineMedicine

Abstract

fetched live from OpenAlex

This study explores English for specific purposes learners’ understanding of technical words in a previously-developed technical word list in Traditional Chinese Medicine (TCM). The principal aim was to estimate what kind of technical terms pose problems to TCM learners and might therefore merit special attention in instruction. Of particular interest was the question whether there is a divergence in the understanding of technical vocabulary in TCM between Chinese and Western background learners. To achieve these aims, a combination of word association tasks and retrospective interviews was implemented with 11 Chinese and 10 Western background TCM learners. The data showed that both Chinese and Western learners encountered certain difficulties in understanding technical vocabulary in their study. However, their sources of difficulty were different. Comparisons of typical word associations between Chinese and Western learners indicated that there was a degree of divergence in the way these two participant groups understood TCM terms.

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)0.001
metaresearch head score (Gemma)0.001
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: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.109
Threshold uncertainty score0.995

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
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 integrity0.0000.001
Insufficient payload (model declined to judge)0.0060.000

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 head0.191
GPT teacher head0.395
Teacher spread0.204 · 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