Is Tourism the Optimal Public Investment to a Small Economy? A Case of Xiao-Liu-Qiu, Taiwan
Bibliographic record
Abstract
Xiao-liu-qiu is one of the offshore islands of Taiwan, it has been promoted as a tourism destination and has attracted more and more tourists since last decade. The aim of this paper is to identify the key industries in the economy of Xiao-liu-qiu, within the context of tourism as a development strategy. To that end, the study utilized the bi-regional input-output analysis to answer the following questions: (1) what are the inter-industry relationship in the economy of Xiao-liu-qiu; (2) how are the economic effects in terms of output, employment, and wage in Xiao-liu-qiu; and (3) whether the economy structure and the multiplier effects had a change during the period 2006-2011 in Xiao-liu-qiu. Indices of economic structure, multipliers, and industry linkages were employed to estimate the importance of each industry, both individually and holistically. In sum, the results show that the economy structure did not have a significant change during the time. The fishery was still a vital industry in the whole economy, but the importance had a slight decrease over the past few years. Tourism characteristic industries were boosting during the period. However, not all of these industries could generate the relatively bigger multiplier effects in terms of output, employment, and wage. Transportation industry played a significant role in the economy when the tourism demand was growing. Both administrative and further research recommendations are given based on the findings.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".