Rethinking overtourism in the post-COVID-19 period: Is demarketing a solution?
Bibliographic record
Abstract
At the end of the first quarter of the 21st century, when international arrivals were increasing, the COVID-19 pandemic occurred and turned into a major health crisis that affected the whole world. It has been emphasized that there is a linear relationship between the spread of the pandemic and travel movements from the time the pandemic started to the present day. In this context, the aim of this study is to determine whether there is a relationship between overtourism and pandemic, which has emerged because of increasing touristic travels in recent years. Another aim of the study is to consider demarketing as a solution proposal in the fight against overtourism to prevent health crises such as COVID-19. In this context, 586 news in which the keywords of COVID-19, overtourism and demarket were used together in a certain period were examined and the obtained data were analyzed with the help of MAXQDA (analysis program for qualitative research) qualitative research analysis program. According to the relations between the themes reflected in the results of the study, demarketing can be expressed as a solution proposal for overtourism in the post-COVID-19 period.
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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 | 0.005 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.010 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".