Bibliographic record
Abstract
In academic disciplines, content rather than writing accuracy is usually emphasized (Hyland, 2013), leaving many English-as-an-additional-language (EAL) students unmotivated to improve writing accuracy. However, the workplace may demand accurate and clear writing. Thus, Ferris (2002, 2011) calls for research into employers’ perspectives on inaccurate and unclear writing of EAL employees to help raise academic faculty and EAL student consciousness. To respond to Ferris' call, this study investigated: 1) employers’ expectations regarding writing accuracy of EAL employees, 2) EAL employees’ language problems in work-related writing, and 3) the impact of writing problems on EAL employees’ employment and career opportunities. The study employed qualitative interviews with ten Canadian employers for data collection and a grounded theory approach for data analysis. Results indicated that the participants generally maintained the same writing standards for EAL and native-English-speaking (NES) employees. The study showed a disconnect between the academic and professional worlds regarding EAL writing standards.
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 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 | 0.000 | 0.000 |
| 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".