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Record W1969515348 · doi:10.5539/elt.v7n10p76

Towards an Analysis of Review Article in Applied Linguistics: Its Classes, Purposes and Characteristics

2014· article· en· W1969515348 on OpenAlexvenueno aff
Ali Sorayyaei Azar, Azirah Hashim

Bibliographic record

VenueEnglish Language Teaching · 2014
Typearticle
Languageen
FieldArts and Humanities
TopicDiscourse Analysis in Language Studies
Canadian institutionsnot available
Fundersnot available
KeywordsApplied linguisticsLinguisticsSubject (documents)Corpus linguisticsField (mathematics)PsychologyEnglish for specific purposesComputer scienceMathematics educationLibrary science

Abstract

fetched live from OpenAlex

The classes, purposes and characteristics associated with the review article in the field of applied linguistics were analyzed. The data were collected from a randomly selected corpus of thirty two review articles from a discipline-related key journal in applied linguistics. The findings revealed that different sub-genres can be identified within the applied linguistic review article genre. Three main types of review articles were therefore identified based on the analysis of linguistic devices often used by the authors, their communicative purposes, and the specialist informants’ feedback: (1) the bibliographic review article which gives readers a comprehensive and descriptive record of annual works and it encompasses the literature-oriented approach, (2) the critical evaluative review article which encompasses subject-oriented approach, that is to say it identifies an idea or raises a research problem, then gives its solution by analyzing and evaluating the selective works done before in the related field, and finally it suggests a new direction, and (3) the mixed-mode review article which has the twin roles and encompasses both literature-oriented and subject-oriented approaches. A possible colony of review genre was suggested and this study further examined some of the characteristics and purposes associated with the review articles. The classification continuum, purposes, characteristics, and linguistic devices of the review articles proposed thus provide useful guidance for graduate EFL (English as a Foreign Language) students and novice writers how to monitor and make use of the review articles during their research writing.

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)0.003
Version: codex-gemma-dda1882f352aValidation 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 score0.457
Threshold uncertainty score0.627

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
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 head0.014
GPT teacher head0.280
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 teacher head, 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

Citations11
Published2014
Admission routes1
Has abstractyes

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