Teaching Pragmatics to L2 Learners for the Workplace: The Job Interview
Bibliographic record
Abstract
This article reports on a pedagogical tool developed to facilitate effective inter-cultural communication in the workplace. We created pre- and post-instruction videos of a native speaker (NS) and non-native speakers (NNSs) in simulated job interviews. Initial interviews were examined for pragmatic difficulties, and one of the researchers also conducted pre- and post-interviews with the recruiters and the NNSs to obtain reactions to the interviews. Their initial videos and post-interview reactions were used to instruct the NNSs in the pragmatics of a job interview. A panel of three expert instructors also watched the pre- and post-instruction videos and rated all interviews on an inventory of specific pragmatic skills. Their ratings were analyzed to determine the candidates' progress and patterns of pragmatic difficulties. Candidates showed marked improvement in their second interviews, demonstrating that the pedagogical intervention used promoted the development of pragmatic competence. Implications for ESL programs, instructors, TESL, and EWP are discussed.
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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 | 0.015 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".