Bibliographic record
Abstract
English in the Workplace (EWP) programs are increasingly surfacing across Can- ada to assist internationally educated professionals (IEPs) with the challenges of integrating into the Canadian workplace. One critical topic of these courses is targeted pragmatics (soft skills) instruction. By learning these skills, IEPs gain valuable tools for communicating effectively and appropriately with their Cana- dian-born colleagues and leaders. The workplace is also becoming increasingly culturally diverse, broadening the required skillsets of IEPs to include intercultural competence—the ability to adapt both cognitively and behaviourally across cultures to achieve communicative goals (Bennett, 1993). As an EWP instructor in a medium-sized institution in Alberta, I worked on the redesign of an EWP course with both pragmatics and intercultural components to be offered online. The course results showed learner development in both pragmatics and intercul- tural competence. In this article, I outline the theory that informed the course design, content, and assessment tools; discuss results of a sample of learners from four pilot offerings; and provide considerations for instructors and instructional designers tasked with the development of online courses of this nature.
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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".