Bibliographic record
Abstract
Purpose The purpose of this paper is to identify and review attempts at mitigation and prevention of overtourism and to outline reasons for the failure to date of such efforts. Design/methodology/approach This paper is a perspective paper and draws on an examination of relevant literature on the subject through the lens of a conceptual framework. It outlines the changing roles of tourism development and marketing organisations and the failure of public sector agencies to control and manage tourism. The varying methods of limiting tourist numbers are examined, and their weaknesses are presented. Findings Conclusions reveal that there are a series of global trends that are contributing to the appearance and continuation of overtourism and which, to date, are proving immune to mitigation and resolution for specific reasons. These include a lack of willingness to accept the problem of tourist numbers and to reduce or effectively manage these at all levels, from local to international. Research limitations/implications Present approaches to mitigation need to be revisited and better integrated with management and control of all aspects of development and framed to achieve and retain political support at all levels. Originality/value There has been little attempt before to analyse the reasons for the failure to effectively mitigate or prevent overtourism, and this paper makes an original contribution in this area in that it is an evaluation of what is known and a summary of shortcomings within the industry and academia.
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 | 0.006 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.008 | 0.010 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".