Bibliographic record
Abstract
The Ministry for Immigration, Refugees and Citizenship Canada includes employment-related language training in its current initiatives. This article aims to contribute to the topic of teaching soft skills for job interviews and job retention, as informed by research from the fields of business and applied linguistics, including pragmatics, as well as insights from personal experience in employment-related classrooms. Ideas for lessons on some critical verbal and nonverbal soft skills are provided for employment-oriented classes at intermediate to advanced levels. Those skills focus on shaking hands, engaging in small talk, and asking questions. Immigration, Réfugiés et Citoyenneté Canada tient compte de l’apprentissage linguistique lié à l’emploi dans le cadre de ses initiatives actuelles. Cet article vise à contribuer au débat sur l’enseignement des compétences générales en lien avec les entrevues et l’embauche, et ce, à la lumière de recherches dans les domaines des affaires et de la linguistique appliquée, y compris la pragmatique et des intuitions tirées d’expériences personnelles vécues lors de cours liés à l’emploi. Des idées de cours sur certaines compétences générales verbales et non verbales essentielles sont présentées pour les cours centrés sur l’emploi aux niveaux intermédiaire et avancé. Ces cours portent sur l’art de serrer la main, de faire la conversation, et de poser les bonnes questions.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.007 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.015 | 0.003 |
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".