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Record W4214748466 · doi:10.5539/ijel.v12n2p70

A Study on Teaching College English Reading from the Perspective of Invitational Rhetoric in China

2022· article· en· W4214748466 on OpenAlexvenueno aff
Xiaomin Yi, Mingyi Bai

Bibliographic record

VenueInternational Journal of English Linguistics · 2022
Typearticle
Languageen
FieldArts and Humanities
TopicDiscourse Analysis in Language Studies
Canadian institutionsnot available
Fundersnot available
KeywordsRhetoricReading (process)Value (mathematics)Perspective (graphical)PsychologyChinaPedagogyMathematics educationSociologyPolitical scienceLawComputer science

Abstract

fetched live from OpenAlex

In this study, we explore the current situation of teaching college English reading in China and offer suggestions for improving it from the perspective of invitational rhetoric, mainly in terms of the three external conditions it advocates, safety, value and freedom, and its core, understanding. We distributed the questionnaire about the current situation of college English reading instruction to students majoring in English and analysed the results. Concerning the conditions of safety, the teachers respected students’ views, but the students were not confident and were unwilling to participate in teacher-student interaction; therefore, teachers should create a safe external environment so that students trust them. Concerning the conditions of value, teachers allowed students to fully express their perspectives, and they prepared and conducted classroom activities from the students’ standpoint. Regarding the conditions of freedom, students were selective in accepting teachers’ views and formulated their own views, but when their views differed from those of teachers, few presented their opinions; teachers must acknowledge their weaknesses at the right time so that students can realise disagreeing with teachers is not equal to offending them. Regarding the conditions of understanding, teachers no longer aimed to instill knowledge; they paid more attention to students’ understanding and encouraged them to form their own ideas while understanding students and accepting reasonable feedback.

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)0.003
metaresearch head score (Gemma)0.005
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: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.077
Threshold uncertainty score0.154

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.003
Science and technology studies0.0090.003
Scholarly communication0.0030.002
Open science0.0010.002
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0030.000

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 head0.022
GPT teacher head0.307
Teacher spread0.285 · 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

Citations1
Published2022
Admission routes1
Has abstractyes

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