Functions of Hedging: The Case of Academic Persian Prose in One of Iranian Universities
Bibliographic record
Abstract
As a feature of academic writing, hedging deals with toning down of scientific claims. There is a clear pedagogical justification for clarification of the concept, especially since it is usually a source of failure in the writing of many foreign/ second language writers of the English language. This problem prompted us to explore it in-depth and see what the underlying assumptions of our academic authors are regarding the issue of hedging. Several studies have aimed at defining and identifying it based upon formal and functional categories (Myers, 1989; Salager Meyers, 1994; Crompton, 1997; Hyland, 1994, 1997, 2005; Lewin 2005, etc.). In the present study, we have tried to investigate the notion in Persian academic prose in two departments of an Iranian university. In order to bring theory into practice, through the text analysis of 32 RAs and some interviews with the writers of the texts under analysis, the question of the function of hedging is studied. It seems that the authors in this study use hedging mainly in its threat-minimizing and politeness functions, which are the social aspects of the issue. Epistemic modality as a cognitive motivation for hedging appears to be less of a concern to the authors under the study. Key words : Hedging; Epistemic modality; Tone down; Knowledge claim
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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 | 0.005 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.008 | 0.009 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".