Hedging and Boosting Sensitivity in EFL Students’ Timed-Handwriting
Bibliographic record
Abstract
Effective use of hedging and boosting is a key aspect of academic writing, enabling writers to express their stance, manage interpersonal meaning, and align with academic discourse norms. Although these rhetorical strategies are well documented in L2 academic and research writing, their application in timed handwritten compositions, particularly among EFL learners in Southeast Asia, remains underexplored. This study fills that gap by examining how EFL university students from Indonesia, Thailand, and the Philippines use hedges and boosters in handwritten essays produced under time constraints. Adopting a mixed-methods approach, the study analyzed 60 compositions using descriptive quantitative analysis to identify frequency patterns and qualitative content analysis to interpret contextual usage. The findings reveal that students display a functional awareness of modality, with hedging mainly realized through adjectives like about and modal verbs like maybe, reflecting caution and generalization avoidance. Boosting was heavily dominated by the adverb always, with less frequent use of definitely and indeed to emphasize certainty and conviction. Although students showed sensitivity in applying these strategies, the overuse of familiar forms and limited structural variety suggests surface-level adaptation rather than advanced rhetorical control—an effect attributed to the cognitive demands of timed writing. The study concludes that explicit instruction in hedging and boosting is crucial for fostering rhetorical awareness, especially in high-stakes or time-limited tasks. It recommends further research into cultural influences, proficiency development, and genre-specific modality use to better support EFL learners’ academic writing competence.
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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 | 0.006 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".