Epistemic Modality in the Argumentative Essays of Chinese EFL Learners
Bibliographic record
Abstract
Central to argumentative writing is the proper use of epistemic devices (EDs), which distinguish writers’ opinions from facts and evaluate the degree of certainty expressed in their statements. Important as these devices are, they turn out to constitute a thorny area for non-native speakers (NNS). Previous research indicates that Chinese EFL learners differ significantly from the native speakers (NS) in marking epistemic modality. One problem of previous studies is that the essay topics are not well controlled, which makes it somewhat ambiguous as to whether the observed linguistic discrepancies are caused by the NNS/NS difference or by the topic differences. This paper sets out to explore much more comparable data from International Corpus Network of Asian Learners of English (ICNALE). The results show that while both NS group and NNS groups are heavily dependent on a restricted range of items, the manipulation of epistemic modality is particularly problematic for the L2 students who employ syntactically simpler constructions and rely on a more limited range of devices, as already discovered in the previous studies. Nevertheless, this study also shows that the most proficient L2 students modify their statements with less certainty markers and more tentative expressions than do their L1 counterparts, and that all learner groups, regardless of their overall language proficiency, use less boosters than L1 writers, which is in sharp contrast with previous studies. The ability to mark epistemic modality has much to do with L2 proficiency. While lower-band students exhibit a heavy reliance on a narrower range of items for strong assertions, higher-band students tend to be more tentative and demonstrate a more native-like use of some Eds. The observed patterns are explained in the light of the inherent properties of English EDs, the imperfect modal instruction and learner factors.
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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 | 0.002 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| 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".