Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Purpose The purpose of this paper is to examine the emergence of overtourism, outline the issues and contributing factors, as it relates to cities, and to suggest possible mitigation measures that might be taken by policy makers. Design/methodology/approach This paper draws from a review of literature looking at longitudinal issues of tourism development overtime and what has contributed to the phenomena of overtourism. A discussion of implications is provided from this review. Findings As tourism is an industry which has historically been poorly managed, greater political will and actual acknowledgement of the problem, as well as action by all levels of government are the necessary first steps to address overtourism. Practical implications This paper outlines key elements that contribute to overtourism and provides global examples which may help practitioners identify key critical issues in their own destinations and identify appropriate actions. Social implications This paper identifies issues raised by local resident populations and possible responses. Originality/value This paper provides a critical overview of overtourism issues, as it relates to cities and discusses potential mitigation and reduction efforts, thereby providing an explanation of why overtourism has become so prevalent.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it