The Pragmatics of Requesting in the Canadian Workplace: A Comparative Investigation of Requests Presented in Workplace ESL Textbooks and Oral Discourse Completion Task Responses
Bibliographic record
Abstract
Within the workplace, pragmatic competence contributes to the performance of difficult speech acts such as requests (Wigglesworth & Yates, 2007).A prevalent concern in teaching second language (L2) pragmatics involves pedagogical materials (Bardovi-Harlig, 2017).Although previous research has found discrepancies in pragmatic language seen in L2 textbooks and elicited responses via discourse completion tasks (DCTs) (e.g.Pablos-Ortega, 2011), workplace language textbooks have seldom been investigated.Therefore, the present study used speech act typologies (Alcón, Safont & Martínez-Flor, 2005;Trosborg, 1995) and criteria for conventional expressions (Bardovi-Harlig, 2012) to identify the most frequently-occurring requests in 17 workplace language textbooks.The same process was applied to 30 native English speakers' elicited, audio-recorded oral DCT responses.Significant differences were found in the frequency of request types identified in the textbooks and elicited responses.The results suggest that workplace language textbooks provide insufficient pragmatic input for L2 learners who are preparing for the workplace.
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 | 0.004 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.009 | 0.004 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".