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Record W2379490556

Spatial Relation of Inbound Tourism Flows from Canadian Provinces to US States

2010· article· en· W2379490556 on OpenAlexaboutno aff
Shu Jing-jing

Bibliographic record

VenueShijie dili yanjiu · 2010
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicRegional Economic and Spatial Analysis
Canadian institutionsnot available
Fundersnot available
KeywordsTourismDistribution (mathematics)GeographyEconomic geographySpatial distributionIndex (typography)CorporationEconomyRegional scienceBusinessEconomicsArchaeology
DOInot available

Abstract

fetched live from OpenAlex

tourist flow from Canada is the largest international flow for US inbounds tourism in North America.Based on the data of Canadian Provinces and US States,this paper analyses the Origin and Destination relation between twelve provinces in Canada and 22 states in US by using the index of tourism preference and market share.Finally,it turns out:arrivals from Canada travel to US states through Great Lakes,high speed railway around America and boundary line;Canadian tourists has a geographical distribution,which means the eastern Canadian tourists distribute in the eastern Great Lakes,the middle Canadian tourists in the nearby states and western tourists in western US. This kind of distribution is influenced by spatial attenuating tendency,high speed railway,airline and high level tourism resources.The above research provides an international tourism corporation experience for Chinese border provinces and its neighboring countries.

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.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.014
Threshold uncertainty score0.070

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0030.007
Science and technology studies0.0010.001
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0040.000

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 head0.011
GPT teacher head0.186
Teacher spread0.175 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2010
Admission routes1
Has abstractyes

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