Towards an Analysis of Review Article in Applied Linguistics: Its Classes, Purposes and Characteristics
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.076 | 0.251 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.066 | 0.045 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.022 | 0.010 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".