Bibliographic record
Abstract
The unprecedented expansion of tourism has given rise to a number of economic, environmental and social impacts that tend to be concentrated in destination areas (Wall & Mathieson, 2006). Tourism research has typically emphasized the economic impacts and yet there are increasing concerns about the effects of tourism on host societies and their environments. A number of techniques have been developed to monitor these impacts. Common analytical frameworks include an environmental audit, environmental impact analysis, carrying capacity, and community assessment techniques. It is beyond the scope of this book to cover these techniques in detail, but the tourism manager needs to have knowledge of the most current models. Managers must also have an understanding of the principles of sustainable tourism, described as “tourism which is developed and maintained in an area in such a manner and at such a scale that it remains viable over an indefinite period and does not degrade or alter the environment (human and physical) in which it exists to such a degree that it prohibits the successful development and well-being of other activities and processes” (Butler, 1993, p. 29). As shown in the Spotlight above, Canadian Mountain Holidays is a good example of this. This increasing emphasis on sustainability has important implications for winter sport tourism, and this chapter focuses on the three pillars of sustainability – the economy, the environment and society. In the past, winter sport tourism was encouraged for its economic benefits with little consideration for the effects on the environment. But this is beginning to change. For tourism to be sustainable, it is vital that its impacts are understood, so that they can be incorporated into planning and management. Table 10.1 lists just some of the positive and negative impacts of winter sport tourism according to experts, many of which are covered in more detail throughout this chapter.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.038 | 0.007 |
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".