Publishers and Authorial Voice in Applied Linguistics Research Articles: A Corpus-Based Study
Bibliographic record
Abstract
Although various studies have been done on authorial voice, none has focused on the possible differences among the articles published by different publishers. To shed light on this issue, this article sought to find the different levels of the use of stance-driven authorial voice in applied linguistics articles published by three international publishers. To this end, 248 applied linguistics articles published from 2000 to 2020 by three intentional publishers were selected through stratified random sampling. After trimming them, a corpus of 1.5 million words was achieved. Then, the normalized frequencies of Hyland’s proposed list of stance markers were extracted using the LancsBox corpus analysis toolbox. The obtained data were analysed using the Kruskal–Wallis test in SPSS 26. The results showed that there are significant differences in the use of overall stance markers, attitude markers, and self-mention markers in the articles published by different publishers, while the results were not significant for hedges and boosters. Our findings can inform the research article writers, academic writing course instructors, and the publishing staff.
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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 | 0.006 | 0.030 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.010 | 0.012 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".