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 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 | 0.019 | 0.036 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.013 | 0.007 |
| Scholarly communication | 0.009 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".