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Record W2963346656 · doi:10.33423/jabe.v21i3.2081

Estimating Tourism Import Demand Elasticities for Four Countries Using the General-to-specific Approach

2019· article· en· W2963346656 on OpenAlex
Ibrahim Mohammed

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.

venuePublished in a venue whose home country is Canada.
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

VenueJournal of Applied Business and Economics · 2019
Typearticle
Languageen
FieldSocial Sciences
TopicDiverse Aspects of Tourism Research
Canadian institutionsnot available
Fundersnot available
KeywordsTourismEconomicsIncome elasticity of demandPrice elasticity of demandContext (archaeology)Econometric analysisElasticity (physics)Econometric modelFinancial crisisEconometricsInternational economicsMacroeconomicsMicroeconomicsGeography

Abstract

fetched live from OpenAlex

This paper applied the general-to-specific econometric modelling technique to estimate demand for tourism imports and the corresponding elasticities for four countries: Australia; Canada; Japan and USA. The findings indicate that tourism imports are generally income and price elastic with values ranging from 1.476 to 1.783 for income elasticity and -1.201 to -1.721 for price elasticity. Furthermore, the findings reveal that, after controlling for seasonality in the data, demand for tourism import is influenced by global financial crisis, disasters and country-specific problems. These findings have important implications for tourism import management, especially in the broader context of promoting tourism imports.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.397
Threshold uncertainty score0.466

Codex and Gemma teacher scores by category

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

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 head0.038
GPT teacher head0.282
Teacher spread0.244 · 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