Online Corpus Tools in Scholarly Writing: A Case of EFL Postgraduate Student
Bibliographic record
Abstract
Some studies have reported the positive outcome of using concordancers and dictionaries in (ESL) context. This study aims to examine how an EFL writer consulted with concordancers and dictionaries along with Google and Google Scholar when engaging in academic writing at university level. The researcher investigated a non-English-major postgraduate student corpus consultation over five months. The researcher provided a toolkit including corpus tools; concordancers, collocation dictionaries, thesaurus, Google, in combination with traditional reference resources such as monolingual and bilingual online dictionaries. The participant received a three-session training to consult with different resources while writing research paper. Real-time data, stimulated recall interview, participants’ writing and query logs served as the main sources of data. Results showed that the participant was aware of the applicability of each corpus tool. He could successfully solve 604 linguistic problems, and promoted his linguistic awareness. It is implied that corpus tools have the potential to assist EFL writers in proofreading and editing the surface levels of their writing.
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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 | 0.008 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.015 | 0.010 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".