Bibliographic record
Abstract
The abundance of diverse and varied tourism economic impact studies can be overwhelming for new researchers in this field. The extensive and heterogeneous nature of these studies often creates confusion regarding the specific study topic, the relevant location, and the appropriate assessment models to employ. This paper employs the systematic literature method, co-occurrence network analysis of author keywords, and crosstable analysis to review 70 articles in the Scopus database from 1988 to April 2021. The result shows that tourism economic impact assessment topics can be grouped into tourism demand and factors affecting tourism demand. Locations of studies consist of nations, regions, cities, towns, and communities. Primary assessment models are Input-Output, CGE, TSA, and SAM; the CGE model and SAM have been applied in nations and regions; TSA has been applied to nations. The Input-Output model can be effectively utilised at different levels, including national, regional, and local scales, encompassing countries, regions, and towns. This study offers a comprehensive panorama of study topics, locations, and appropriate measurement models for economic impact assessment, enabling scholars to delve into further research with a clear understanding and direction.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.023 | 0.022 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.019 | 0.002 |
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".