Bibliographic record
Abstract
Purpose Education institutions can be slow to react to the changes that are happening in human capital development content and delivery. This article highlights some of the shifts that robotics, artificial intelligence (AI) and access to information are having on jobs in tourism and the future of work. It explores the ways in which the tourism education sector can respond. Design/methodology/approach This paper draws upon content shared at several conferences and webinars addressing the future of work and the education delivery methods from experts and commentators on the subject. This was augmented by research conducted by global tourism associations, the World Economic Forum and other global associations and supported with secondary data from recent media and online content providers. Findings By highlighting emerging trends in the sector and skills to thrive in the fourth industrial revolution, we can identify what education should focus on during this period of transition and uncertainty. We need to capitalize on the digital delivery skills we have developed due to COVID-19 and build new content and accessible learning approaches. Originality/value There are many uncertainties about the future of work and the way that a rapidly digitized education delivery approach has and will affect tourism education in the future. This article is aimed to generate further thought and dialogue by identifying changes and raising points about what we are effective at in public post-secondary education and what we need to capitalize on and adapt to in the future. The core question posed is that if the tourism and hospitality workforce and work environment has changed, has, or can, tourism and hospitality training and education change as well?
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.007 | 0.018 |
| Scholarly communication | 0.012 | 0.011 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.012 | 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".