Introduction
Bibliographic record
Abstract
This co-authored book was researched and written during a time that few had foreseen, let alone prepared for. The impacts of Covid-19 are being felt across the world’s societies, economies and natural environment. Some industries have been more impacted than others, including the international tourism industry. The United Nations World Tourism Organisation (UNWTO) predicts that due to the travel related impacts of Covid-19 international tourism could decline by between 60-80% in 2020, with US$80 billion already lost in exports from the industry for the first quarter of 2020 (UNWTO, 2020a). In these unprecedented times, it becomes more important than ever to consider what the future might hold for the industry. By examining current and future capabilities of the industry, this research book explores the opportunities available to shape the future through rebuilding, disrupting and developing greater resilience in the tourism industry. The common theme throughout the chapters is change – no matter how change emerges, the authors of this book recognise that the industry is always going to face times of turbulence, whether it be climate change, political or financial disruptions or pandemics, those in the industry need to have resilience, understand the forces of change and be prepared to adapt. This chapter sets out the core principles associated with anticipating the future of the international travel, hospitality and events sectors. It starts with a broad overview of the global tourism industry, followed by the definitions and scope of the sectors that will be covered in the book. A discussion on tourism futures as an area of research is presented and finally, the sections and individual chapters are introduced.
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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.384 | 0.250 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".