Stakeholders' Perceptions of Culinary Programs in Ontario Community Colleges
Bibliographic record
Abstract
The hospitality industry in Canada is growing. With that growth is a demand for qualified workers to fill available positions within all facets of the hospitality industry, one ofthem being cooks. To meet this labour shortage, community colleges offering culinary arts programs are ramping up to meet the needs of industry to produce workplace-ready graduates. Industry, students, and community colleges are but three of the several stakeholders in culinary arts education. The purpose of this research project was to bring together a cross-section of stakeholders in culinary arts education in Ontario and qualitatively examine the stakeholders' perceptions of how culinary arts programs and the current curriculum are taught at community colleges as mandated by the Ministry of Training, Colleges and Universities (MTCU) in the Culinary Program Standard. A literature review was conducted in support of the research undertaking. Ten stakeholders were interviewed in preliminary and follow-up sessions, after which the data were analyzed using a grounded theory research design. The findings confirmed the existence of a disconnect amongst stakeholders in culinary arts education. Parallel to that was the discovery of the need for balance in several facets of culinary arts education. The discussions, as found in Chapter 5 of this study, addressed the themes of Becoming a Chef, Basics, Entrenchment, Disconnect, and Balance. The 8 recommendations, also found in Chapter 5, which are founded on the research results of this study, will be of interest to stakeholders in culinary education, particularly in the province of Ontario.
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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 | 0.003 | 0.008 |
| 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 | 0.005 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".