Online Corpus Tools in Scholarly Writing: A Case of EFL Postgraduate Student
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it