Canadian chefs’ workplace learning
Bibliographic record
Abstract
Purpose – This paper aims to examine the formal and informal workplace learning of professional chefs. In particular, it considers chefs’ learning strategies and outcomes as well as the barriers to and facilitators of their workplace learning. Design/methodology/approach – The methodology is based on in-depth, face-to-face, semi-structured interviews with 12 executive chefs from a variety of restaurant types. Chefs were asked questions that focused on how they learned, the learning outcomes that they experienced and factors that inhibited or facilitated their learning. Findings – Findings suggest that the strategies, outcomes, barriers and facilitators experienced by professional chefs are similar in many respects to those of other occupational/professional groups. However, there were some important differences that highlight the context of chefs’ workplace learning. Research limitations/implications – The sample, which is relatively small and local, focuses on one city in Canada, and it is limited in its generalizability. Future research should include a national survey of professional chefs. Originality/value – Using a qualitative approach, this in-depth study adds to the literature on workplace learning, strategies, outcomes, barriers, facilitators and context factors by addressing a relatively understudied profession.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.015 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.019 | 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".