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Record W3173760208 · doi:10.1177/13548166211010659

Business cycles and tourism imports in the South Pacific

2021· article· en· W3173760208 on OpenAlex
Puneet Vatsa, Franklin G. Mixon, Kamal P. Upadhyaya

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

VenueTourism Economics · 2021
Typearticle
Languageen
FieldSocial Sciences
TopicDiverse Aspects of Tourism Research
Canadian institutionsnot available
Fundersnot available
KeywordsTourismQuarter (Canadian coin)Business cycleEconomicsBusinessEconomyEconomic geographyGeographyMacroeconomics

Abstract

fetched live from OpenAlex

The demand for international tourism in Australia and New Zealand is vital to the South Pacific’s tourism-reliant islands. However, at the time of this study these two countries find themselves in precarious economic situations. The question addressed by this study is, will tourism imports in these two countries pick up on the back of economic recovery? We answer this question using time-difference analysis and the newly developed Hamilton filter. The short answer is yes, but more so in New Zealand than Australia. The key findings of this study are that tourism demand in both Australia and New Zealand is pro-cyclical, tourism demand cycles in New Zealand strongly lag business cycles by 1 year, whereas in Australia, they weakly lag business cycles by one quarter, and, overall, tourism demand and business cycles in New Zealand share a stronger association than they do in Australia.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.147
Threshold uncertainty score0.475

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 studies0.0000.000
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 head0.026
GPT teacher head0.278
Teacher spread0.252 · 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