Investigating Methodological Trends of Hedging Strategies in Academic Discourse: A Systematic Literature Review
Bibliographic record
Abstract
This systematic literature review investigated the methodological trends regarding hedging strategies in academic discourse from 2014 to 2023. A total of 40 peer-reviewed empirical studies were analyzed, focusing on aspects such as publication year, regional participation, sample size, genre type, research design, category, discipline, section, and analytical framework. The review revealed a fluctuating interest in hedging strategies, peaking in 2023, with significant contributions from Asia and Europe. Research articles dominated the genre types, reflecting a preference for standardized and accessible formats. Mixed methods were the most common research design, followed by quantitative and qualitative approaches. Mono-disciplinary studies were prevalent, highlighting detailed analyses within specific fields, whereas cross-disciplinary, cross-cultural, cross-linguistic and cross-generic studies emphasized comparative approaches. The research spanned both hard and soft sciences, with applied linguistics and chemistry being notably represented. Comprehensive examinations of all sections of academic texts, particularly the discussion section, were frequent. Established frameworks, primarily those by Hyland (1998) and Salager-Meyer (1994), were commonly utilized, underscoring their influence. This review highlighted the need for more cross-disciplinary and cross-generic analyses and the development of comprehensive frameworks to enrich the understanding of hedging strategies in academic writing. Future research should expand cross-disciplinary, cross-cultural, and regional analyses, develop new frameworks, explore emerging genres, integrate technology, diversify methodologies, and address geographical disparities to broaden the global understanding of hedging in academic discourse.
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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 | 0.063 | 0.220 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.038 | 0.032 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.007 | 0.010 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".