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Record W2527757885 · doi:10.5539/elt.v9n11p29

Building a Negotiation Mechanism in EFL Classroom in Chinese Context: Concepts and Strategies

2016· article· en· W2527757885 on OpenAlexvenueno aff
Shiyong Jiang

Bibliographic record

VenueEnglish Language Teaching · 2016
Typearticle
Languageen
FieldSocial Sciences
TopicChildren's Rights and Participation
Canadian institutionsnot available
Fundersnot available
KeywordsNegotiationPsychologyContext (archaeology)Mechanism (biology)ChinaEnglish as a foreign languageMathematics educationProcess (computing)Foreign languageFace (sociological concept)PedagogyLinguisticsSociologyComputer sciencePolitical scienceEpistemology

Abstract

fetched live from OpenAlex

Foreign language teaching is a bilateral process in which both the teacher and students must play their roles effectively. However, lack of interaction is the problem many EFL teachers in China face. To make the teaching more effective, negotiation in the classroom becomes a must. This paper, based on the current situation of English teaching in China, probes into negotiation mechanism in EFL classroom and provides suggestions on how to apply negotiation mechanism in EFL classroom in China.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.005
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.010
Threshold uncertainty score0.046

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0020.001
Science and technology studies0.0050.009
Scholarly communication0.0070.009
Open science0.0020.004
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0040.000

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 head0.011
GPT teacher head0.327
Teacher spread0.317 · 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 source (direct Gemma or distilled Codex), 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

Citations2
Published2016
Admission routes1
Has abstractyes

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