Metadiscourse in Research Article Genre: A Cross-Linguistic Study of English and Hausa
Bibliographic record
Abstract
The aim of conducting this study came from a need to explore contrastive study in using metadiscourse features between English and Hausa in research article genre. This study investigated what metadiscourse features are frequently used across two languages in research article genre. A sub-corpus of ten research articles was compiled from each language. The study adopted Hyland’s (2005) typology of metadiscourse features. The results of the study show that there are certain commonalities and differences in using the features across the languages. In terms of similarity, both groups of writers typically used all categories of metadiscourse features. They are almost having a similar frequency of boosters and attitude markers. On the other hand, writers from Hausa research article typically had a high frequency of self-mention, whereas writers from English had a low frequency of the feature. One remarkable feature in Hausa sub-corpus is the use of proverbs and idioms. This study recommends raising awareness of students in relation to linguistic and social conventions of their disciplines.
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 imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".