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Record W2407945866 · doi:10.11821/yj2011030006

Analysis of tourism labor's inter-industry mobilty rules based on comparison among five areas at home and abroad

2011· article· en· W2407945866 on OpenAlex
Han Guosheng

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

aboutThe title or abstract carries a Canadian signal from the geographic lexicon.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueGeographical Research · 2011
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicHospitality and Tourism Education
Canadian institutionsnot available
Fundersnot available
KeywordsTourismChinaDominance (genetics)BusinessLabor mobilityRanking (information retrieval)AgricultureGeographyDemographic economicsEconomic growthLabour economicsEconomics

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.012
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.003
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.049
GPT teacher head0.333
Teacher spread0.284 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it