Human capital challenges in the food and beverage service industry of Canada
Bibliographic record
Abstract
Purpose Challenged by a clear shortfall of available employees to be long-term members of the food service industry, this paper aims to establish reasons for the shortage of available employees and curate a number of strategies to improve the situation. Design/methodology/approach This paper draws on the perspectives of many industry stakeholders. These professionals collaborated to identify a number of contributing factors to the shortage of employees in the Canadian food and beverage industry. Corresponding solutions were assessed, prioritized and categorized by groups responsible for taking action. Findings There are many strategies that can be implemented in both the short and long term that can increase the draw for potential employees to join this industry. Practical implications Industry members, educators and government policymakers can all play a role in improving the worker shortage in the food service industry. The recommendations range from industry collaboration to redefinition of jobs and to redistribution of wages. Originality/value The co-authors of this paper include the President and CEO of Ontario Restaurant, Hotel and Motel Association and educators with strong industry experiences gained in the positions of food and beverage director, restaurant manager and executive chef. Given the diverse experiences of the author team, this paper creates a more holistic view of the recommendations to consider for this industry to see positive change.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".