Human capital challenges in the events industry of Canada: finding innovative solutions
Bibliographic record
Abstract
Purpose This paper aims to provide perspectives on human capital challenges for the events industry in Canada. Industry and educator perspectives are featured in two segments of the paper. Design/methodology/approach This paper provides an overview of the events sector in Canada and includes a literature review clarifying key definitions and terms. Industry and educator perspectives highlight ongoing discussions related to some of the human capital challenges identified in the paper. Findings This paper explores challenges related to human capital such as the pressures of working in the events industry and finding a work – life balance given the demands of the profession. Human capital challenges related to the preparedness of professionals and the need for continued certification and training are also discussed. A potential solution considers licensing and industry-wide certification. Consideration of the benefits and requirements of industry-wide certification and licensing is ongoing. Practical implications This paper emphasizes the need for cooperation between industry and educators to ensure that new events professionals have the necessary skills training and can recognize the need to contribute to the events industry throughout their careers. Originality/value This paper considers perspectives from education and industry and emphasizes challenges that are relevant and current for existing and future events professionals in Canada.
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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 | 0.001 | 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 | 0.000 |
| 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".