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Record W4360989425 · doi:10.5539/elt.v16n4p81

A Study on Strategies to Enhance the Status of English Subject

2023· article· en· W4360989425 on OpenAlexvenueno aff
Ao Guo

Bibliographic record

VenueEnglish Language Teaching · 2023
Typearticle
Languageen
FieldArts and Humanities
TopicSecond Language Learning and Teaching
Canadian institutionsnot available
Fundersnot available
KeywordsDisciplineCollege EnglishMathematics educationSubject (documents)Perspective (graphical)ChinaPedagogyDoctrineEnglish studiesPsychologySociologyProfessional developmentTest of English as a Foreign LanguageEnglish languagePolitical scienceSocial scienceLinguisticsLawLibrary scienceComputer science

Abstract

fetched live from OpenAlex

The status of the discipline is the key to its development. This paper investigates the current status of the subject of English in high schools and argues that its reduced disciplinary status will have a profound impact on the development of English. From the perspective of an independent discipline, high school English teaching is a relatively independent discipline. The disciplinary position is both a matter of doctrine and jurisprudence. Because it has not been given its proper disciplinary status for a long time, high school English teaching used to be introduced as a flexible teaching mode in the profession of talent training. With the in-depth development of the reform of English teaching in high schools in China, the discipline construction of English teaching in colleges and universities needs more attention, and it is suggested that English is strengthened as a professional course, and the status of English discipline is continuously enhanced and upgraded.

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)0.002
metaresearch head score (Gemma)0.007
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.018

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0020.002
Scholarly communication0.0040.003
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.001

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 head0.021
GPT teacher head0.297
Teacher spread0.277 · 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 designNot applicable
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
Published2023
Admission routes1
Has abstractyes

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