The Impact of New Tourism Models on People’s Living Standards
Bibliographic record
Abstract
The backdrop of this study stems from the profound impact of COVID-19 on the tourism sector in 2020. Post-pandemic, nations globally embarked on robust tourism development efforts. Drawing on data from the 2020-2023 Urban Economic Report, this research delves into discerning disparities between emerging and traditional tourism paradigms. Employing a multi-case comparative analysis approach, taking Iceland, Vancouver, and Harbin as the research objects, it examines the ramifications of post-COVID-19 tourism strategies implemented by three distinct tourist destinations on resident livelihoods. By encapsulating diverse impacts, the study amalgamates findings from these cases for a comprehensive comparison. It serves to elucidate shortcomings while elucidating the merits of novel tourism models, thereby offering insights crucial for the sustainable evolution and innovation of the tourism industry. This research serves as a compass for tourism practitioners, facilitating a nuanced understanding of emerging trends and contributing to informed decision-making in navigating the path toward industry resilience and growth.
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".