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Record W3095961483 · doi:10.5539/ijel.v11n1p10

Move Structures and Cognitive Genres in the Methods Sections of Experimental Research Articles and Corpus-Based Studies in Applied Linguistics

2020· article· en· W3095961483 on OpenAlexvenueno aff
Wei-chen Chuang, Chia‐Chi Chen

Bibliographic record

VenueInternational Journal of English Linguistics · 2020
Typearticle
Languageen
FieldArts and Humanities
TopicDiscourse Analysis in Language Studies
Canadian institutionsnot available
Fundersnot available
KeywordsCognitive linguisticsSection (typography)Variation (astronomy)CognitionComputer scienceApplied linguisticsCorpus linguisticsGenre analysisContrast (vision)Research methodLinguisticsNatural language processingPsychologyArtificial intelligencePhysicsPhilosophy

Abstract

fetched live from OpenAlex

This paper reports an investigation of the variation in the move structures and cognitive genres found in the methods sections of research articles (RAs) in a discipline with diversified research methods. Sixty-five RAs involving two research methods in applied linguistics, namely corpus-based studies and experimental research, were analyzed. The results showed that the methods sections of these RAs contained distinct move structures. These distinct move structures, along with the findings of previous studies on the move structures of various disciplines, suggested that the research method adopted in a study is a stronger factor than discipline is in determining the move structure of the methods section. The analysis of the cognitive genres used in the methods sections showed that authors typically require more than one cognitive genre to realize the major moves of the methods sections. However, this variation in the cognitive genres is low between the two patterns of study. These findings suggested that different research methods yield variations in the move structures of the methods sections. However, the cognitive genres that authors require to realize the move structures tend to be common.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.070
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.420
Threshold uncertainty score0.938

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.070
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.147
GPT teacher head0.454
Teacher spread0.307 · 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 teacher head, not a consensus.

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

Citations0
Published2020
Admission routes1
Has abstractyes

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