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Record W3178685848 · doi:10.22215/etd/2020-14260

A Triangulated Accounting of Top Notch 2: Negotiating Ideologies in the Multimodal Discourse of an EFL Textbook in Korean University Classrooms

2020· dissertation· en· W3178685848 on OpenAlexaff
Christopher A. Smith

Bibliographic record

Venuenot available
Typedissertation
Languageen
FieldArts and Humanities
TopicDiscourse Analysis in Language Studies
Canadian institutionsCarleton University
Fundersnot available
KeywordsCritical discourse analysisIdeologyNegotiationSociologyPedagogyCurriculumHegemonyClass (philosophy)MultimodalityPoliticsMathematics educationLinguisticsPsychologyPolitical scienceSocial scienceComputer science

Abstract

fetched live from OpenAlex

Textbooks are artifacts of a pedagogical culture, but in the context of English as a Foreign Language (EFL) education in Korean universities, that culture often forces instructors and students to use generic, global publications for reasons more political than pedagogical. Critical studies of EFL textbooks in global and Korean contexts reveal they contain certain social realities that often favor Anglo-centric hegemonies, while marginalizing their intended audiences. Regardless of those social injustices, Korean university programs continue to prescribe globally published EFL textbooks that often serve as the course curriculum. While some research underscores content and consumption in Korean contexts, none yield a comprehensive look at the multimodal discourse in a specific EFL textbook or correlate how that content is negotiated, consumed and valued by students and instructors in a comparable fashion.

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)0.014
metaresearch head score (Gemma)0.022
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 score0.016
Threshold uncertainty score0.072

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0140.022
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0060.005
Science and technology studies0.0070.010
Scholarly communication0.0120.011
Open science0.0020.009
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0080.001

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 head0.019
GPT teacher head0.280
Teacher spread0.261 · 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

Citations22
Published2020
Admission routes1
Has abstractyes

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