Analysis of tourism labor's inter-industry mobilty rules based on comparison among five areas at home and abroad
Bibliographic record
Abstract
Tourism employment has many and negative characteristics,which play a particular role in tourism labor's inter-industry mobility under different social backgrounds.Taking Jiuzhaigou as a case study,the article,through a comparative study in the existing work on mobility in Hungary,Somerset and Coventry in the United Kingdom,Jiuhua Mountain in China and Vancouver Island in Canada,analyzes tourism labor's mobility pattern,self-evaluation of mobility impacts,and mobility motivations under different backgrounds.The findings of the study are as follows.First,labor comes from an unusually wide range of industries.In foreign countries,the highest percentage engaged in trade(Wholesale and Retails Trade),and public sector such as public administration,and education and health contributed a high proportion,and mobility from declining industries was not insignificant,approximately accounting for 10%.In China tourism draws labor mainly from the traditional sectors such as agriculture and manufacturing,and high proportion of unemployed and female young labors are inclined to work in tourism.Second,the most impact of mobility was reported on the job satisfaction variables.The dominance of job satisfaction and physical environment may have been traded off for poor income,long working hours and job/education match.As is indicated by the multi-regression analysis,the satisfaction is mainly supported by career prospects,living standards,working hours and physical environment in China.Third,factor analysis of 30 motivation variables confirms five-dimensional structure.The means' ranking of motivation and factor display that labor mainly arrives by positive attributes associated with this industry and few are absorbed for refuge.In China instrumental utility together with positive is the strongest motivational forces.Entrepreneurial is correlated with businessman moving from agriculture and service industry.Instrumental utility and positive are most approved by all kinds of samples,but refuge approved least.These rules result from the combined effort exerted by the three powers of tourism employment's characteristics,regional socio-economic backgrounds and case's features.
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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 | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".