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Record W4282960666 · doi:10.5430/wjel.v12n5p327

On the Selection of English Discourses for Chinese College Entrance Examination in Recent Five Years (2017-2021)

2022· article· en· W4282960666 on OpenAlexvenueno aff
Li Jian, Nengwei Fan

Bibliographic record

VenueWorld Journal of English Language · 2022
Typearticle
Languageen
FieldComputer Science
TopicEducational Technology and Assessment
Canadian institutionsnot available
FundersGovernment of Jiangsu Province
KeywordsExposition (narrative)Argumentation theorySelection (genetic algorithm)Scope (computer science)ChinaNarrativeSociologyWorld EnglishesCurriculumPolitical scienceLinguisticsPedagogyMedia studiesLiteratureLawComputer sciencePhilosophyArt

Abstract

fetched live from OpenAlex

The paper mainly focuses on the selection of English discourses in the National College Entrance Examination papers of China in recent five years. It is found that there are four discourse types of application, narration, exposition and argumentation. The thematic contexts of the discourses in the papers involve the three categories of “man and self”, “man and society”, “man and nature”,which echoes The National English Curriculum Standard (2017 Edition and 2020 Revision). In the papers, Chinese traditional culture is strengthened and the source of material selection is not limited to Britain and the United States, but expanded to the other countries in the world. The general trend in discourse materials reflects that the scope of material selection is more extensive with more attention to Chinese excellent traditional culture and multi-culture of the world for assessing culture awareness of the students.

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)0.006
metaresearch head score (Gemma)0.019
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.018
Threshold uncertainty score0.032

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.019
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0180.013
Science and technology studies0.0030.001
Scholarly communication0.0040.002
Open science0.0010.003
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0050.001

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 head0.008
GPT teacher head0.274
Teacher spread0.266 · 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 designObservational
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
Published2022
Admission routes1
Has abstractyes

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