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Record W2911392569 · doi:10.5539/ijel.v9n2p15

How Experimental Psychologists Write the Method Sections of Journal Articles

2019· article· en· W2911392569 on OpenAlexvenueno aff
Weijen Zhuang

Bibliographic record

VenueInternational Journal of English Linguistics · 2019
Typearticle
Languageen
FieldArts and Humanities
TopicDiscourse Analysis in Language Studies
Canadian institutionsnot available
Fundersnot available
KeywordsSection (typography)CognitionField (mathematics)Inclusion (mineral)Computer sciencePsychologyExperimental psychologyResearch methodCognitive psychologyMathematics educationEpistemologySocial psychologyMathematics

Abstract

fetched live from OpenAlex

This paper reports an investigation on the move structures and cognitive genres found in the methods sections of research articles (RAs) in the field of experimental psychology. Thirty RAs of experimental psychology were analyzed in terms of the move structure and the cognitive genre applied to realize the moves. The analysis of the move structure of these research articles showed that the basic moves were: describing participants, describing data collection procedure(s) and explaining data analysis. 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. The most commonly used cognitive genres in realizing the method section of a RA of experimental psychology were explanation and report. This suggests that the inclusion of the instruction of explanation and report in the EAP writing courses for the psychology major students is essential.

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)0.054
metaresearch head score (Gemma)0.298
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Scholarly communication
Consensus categoriesnone
DomainCandidate signal: Reporting · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.989
Threshold uncertainty score0.287

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0540.298
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0040.002
Science and technology studies0.0030.004
Scholarly communication0.0110.009
Open science0.0020.002
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0120.006

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 head0.035
GPT teacher head0.345
Teacher spread0.311 · 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.

Study designObservational
DomainReporting
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
Published2019
Admission routes1
Has abstractyes

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