HUMAN RESOURCES NEW CHALLENGES FOR CANADIAN EMPLOYERS - ANALYSIS OF THE HOSPITALITY AND TOURISM INDUSTRIES STRUGGLING THROUGH 2022
Bibliographic record
Abstract
The emergence of the coronavirus disease (COVID-19) in December 2019 was a wake-up call for the human resources community. In some ways, COVID-19 was a real game changer, but in other ways, it was simply another flashpoint in an ongoing HR transformation. Many have lost their jobs, and employers and their human resources departments have had to adjust to the situation. Some industries succeeded in going through the pandemic while others did not. It is no wonder why many employees have reoriented their careers to other industries, especially those who used to work in tourism and hospitality. Today, these industries are facing more challenges than ever before, and the lack of employees is making the HR job more difficult and, in many cases, impossible to find employees. This paper is about the HR challenges before and after the pandemic. It is aimed at exploring how human resources management has evolved and changed in the context of the COVID-19 pandemic. From a historical perspective, we will first provide an overview of the history of human resources management in relation to the pandemic and then discuss the contemporary issues that have emerged following the outbreak. Then we will analyze the HR situation in Canada and how the pandemic has affected it and its backdown on hospitality and tourism. Article visualizations:
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.008 |
| Science and technology studies | 0.012 | 0.002 |
| Scholarly communication | 0.008 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 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".