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Record W3175686979

Functions of Hedging: The Case of Academic Persian Prose in One of Iranian Universities

2012· article· en· W3175686979 on OpenAlexvenueno aff
Fariba Ghazanfari, Bistoon Abassi

Bibliographic record

VenueStudies in literature and language · 2012
Typearticle
Languageen
FieldArts and Humanities
TopicDiscourse Analysis in Language Studies
Canadian institutionsnot available
Fundersnot available
KeywordsPolitenessLinguisticsPsychologyPersianTone (literature)Modality (human–computer interaction)Function (biology)EpistemologyAcademic writingOrder (exchange)Epistemic modalityCognitionSociologyMathematics educationPhilosophyComputer scienceEconomics
DOInot available

Abstract

fetched live from OpenAlex

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

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 imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.132
Threshold uncertainty score0.334

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.033
GPT teacher head0.305
Teacher spread0.273 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
Domainnot available
GenreEmpirical

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".

Quick stats

Citations3
Published2012
Admission routes1
Has abstractyes

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