A Corpus-Based Study on the Use of Reporting Verbs in Applied Linguistics Articles
Bibliographic record
Abstract
Reporting verbs is one of the most important issues in writing academic paper because they are used to express the process and reliability of claims to support authors’ writing. Therefore, the current study aimed at investigating (1) the most frequently used category of reporting verbs in applied linguistic articles and (2) how the category used in the citation process is used. 52 articles from three applied linguistic journals were analyzed using Antconc software’s concordance function. This study focused on reporting verbs used in the literature review section since it consists of more reporting verbs than other sections in articles. The reporting verbs in the articles were analyzed into a concordance line and then were classified into Hyland’s Framework of reporting verbs (2002). The results of the study showed that the uses of reporting verbs were classified into research acts, which was the most frequent use of reporting verbs, discourse acts, and cognition acts respectively. The study also presented the frequently used of reporting verbs in different subcategories of the research, discourse, and cognition acts. Additionally, reporting verbs were examined to investigate the verb forms and voices used in applied linguistic articles. The use of reporting verbs according to Hyland’s (2002) framework, verb forms, and voices are also discussed.
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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 | 0.020 | 0.092 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.033 | 0.038 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".