Reader Engagement in English and Persian Applied Linguistics Articles
Bibliographic record
Abstract
There is an increasing interest in the way academic writers establish the presence of their readers over the past few years. Establishing the presence of readers or what Kroll (1984, P.181) calls imagining “a second voice” is accomplished when a writer refers explicitly to their readers using explicit linguistic resources (reader engagement markers). Although there are some cross-disciplinary studies and only one cross-cultural study (Hinkel, 2002) which has investigated how writers in different disciplines/cultures acknowledge the presence of their readers, no contrastive study has ever been reported to have examined how academic writers from Persian and English writing cultures address their readers in their texts.Drawing on 60 applied linguistics articles (20 English articles written by native English applied linguists, 20 English articles written by native Persian applied linguists and 20 Persian articles written by native Persian applied linguists), this study aimed at seeing how native Persian and English writers engage their readers in their articles. Hyland’s (2005a) interactional model of stance and engagement was used as an analytical framework to identify the type and frequency of reader engagement markers in these three groups of articles. The result of the analysis showed significant differences in the way native Persian and English represent their readers. Also, considerable differences were observed in categorical distribution of reader engagement markers.
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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 | 0.003 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
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".