Interaction in Research Discourse: A Comparative Study of the Use of Hedges and Boosters in PhD Theses by Australian and Saudi Writers
Bibliographic record
Abstract
Like any other discourses, academic discourses are also not completely objective manuscripts and quite often overtly and/or covertly express their writers’ intended stances. Hedges and boosters are significantly common rhetorical strategies employed frequently by writers to attenuate or reinforce the propositional intensity of the texts to establish an interactional rapport with the readers/receivers. These interactional features are referred to as metadiscourse by Hyland (2018), who systematically categorizes such rhetorical strategies in the form of a taxonomy. Utilizing this taxonomy, the current study focused on the comparative analysis of hedges and boosters in Ph.D. theses written by Saudi and Australian writers at Monash University, Australia. This specialized corpus-based analysis identified the cross-cultural differences in employing hedges and boosters within academic discourse. The findings suggest that there are significant differences in the use of hedges and boosters between native and non-native speakers of English. Non-native speakers tend to use more hedges than boosters, while native speakers use more boosters than hedges. Overall, the natives’ discourse appears to be more interactional than the non-native writers based on the analysis of the statistical differences that emerged.
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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 | 0.010 | 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 | 0.005 | 0.005 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.005 |
| 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".