Bibliographic record
Abstract
Because of the undefined boundaries of the tourism industry,the objective of tourism statistics has been largely to provide feedback to individual trading groups,rather than to the interpretation and analysis of a highly standardized production activity.This apparent limitation within tourism statistics makes it difficult to gauge the scale and status of the tourism industry.Since the closing years of the last century,the Tourism Satellite Account(TSA) framework has gradually become the internationally accepted scientific method for conducting statistical investigations of tourism.However,there are a number of constraints associated with this method.Firstly,the compilation of a TSA requires a large-scale sample survey and considerable costs in terms of capital and human and physical resources.Secondly,the results obtained from the application of this method entail a certain degree of deviation from people's daily experiences with which tourism is evidently more closely associated compared with industrial activities and productive service activities.Thirdly,the challenges of matching the statistical results of a TSA with the demands of sectionalism are considerable compared with the simpler consideration of gross income from tourism and its proportionate contribution to GDP as indicators of the size of the tourism industry.However,in terms of accuracy,gross income from tourism as a proportion of GDP provides only a speculative measure of the industry's total economic contribution.As an indicator,it does not meet the requirements of the principles of statistics.Consequently,China's macro statistics on tourism present a dilemma in the long term:on one hand,they are accurate but not good to use; on the other hand,they are good to use but not accurate.This dilemma has led to the inability of the tourism industry to ascertain its own status.It has also failed to penetrate research discourses on the national economy and industry.Moreover,some evident errors in theory and practice regarding the calculation of the scale of the tourism industry have resulted in superficial perceptions about the tourism economy within society,but rarely in rational quantitative support.A discussion on the pros and cons of statistical methods used to investigate tourism is,in our view,futile.Instead,we believe it is necessary to extend beyond this discussion to initiate action relating to statistical accounting of the tourism industry.This is important,because it is possible to calculate the main index of the TSA based on China's current statistical system.In this study,we combine a TSA and input-output theory with a survey of inbound and domestic tourism.We develop a set of algorithm processes to account for added value of the tourism industry that meet the standards of international statistics and the TSA.Our findings indicate that the added value of the tourism industry accounts for 2.67% of China's GDP; a figure that is comparable to those obtained for the United States,Canada,and several other countries.It should be noted that this proportion reflects only the direct contribution of the tourism industry to the total national economy,and does not include indirect impacts due to associated effects.One factor that may commonly be excluded from the calculation process is that of sales prices in the tourism survey data,with the compilation of input-output tables being based on producer prices.We eliminated the significant difference of prices through targeted conversion according to tax rates for different industries.The algorithm process developed in this study contributes to an objective and accurate understanding of the status of the tourism industry in the national economy.It provides a precise and effective basic framework for calculating the full impact of the tourism economy using a computable general equilibrium model and an input-output price effect model.
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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 | 0.003 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.009 | 0.017 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.010 | 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".