Bibliographic record
Abstract
Events play a significant role in today’s society, and for tourism destinations they are important due to their tourist, social and cultural functions (Getz, 2007), as well as their role in local and regional development (Wood, 2005). First and foremost, events are a great anchor for attracting tourism, providing tourists with a prime opportunity to get to know the local culture and experience the essence of the place. During an event, visitors have a unique chance to interact with the local community, gaining a deeper experience of the ambience, customs and local culture. Events can also help in improving a place’s image, creating a window for positive media coverage. Finally, for the residents themselves, events are a unique occasion to celebrate the local culture and interact within the community – you can see examples of this in the opening Spotlight above. According to Jackson (2013), three industries in particular are shaping the growth of the events sector (see Figure 9.1). Firstly, the hospitality industry - be it hotels, restaurants or venues - has viewed events as a way of encouraging new clientele or increasing the yield of existing customers. This is the case for the World Ski and Snowboard Festival held in Whistler, Canada every April in order to increase occupancy rates at the end of the winter season. Hotel rooms are fully booked during the event, which spans two weekends in order to maximize occupancy rates.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.017 | 0.007 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.061 | 0.011 |
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".