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Record W2328026057 · doi:10.5367/te.2014.0402

<i>Research Note:</i> Nowcasting Tourist Arrivals in Barbados – Just Google it!

2014· article· en· W2328026057 on OpenAlexaboutno aff
Mahalia Jackman, Simon Naitram

Bibliographic record

VenueTourism Economics · 2014
Typearticle
Languageen
FieldMedicine
TopicData-Driven Disease Surveillance
Canadian institutionsnot available
Fundersnot available
KeywordsNowcastingTourismThe InternetAutoregressive modelEmpirical evidenceEconometricsAdvertisingRegional scienceEmpirical researchEconomicsBusinessComputer scienceGeographyWorld Wide WebStatisticsMathematics

Abstract

fetched live from OpenAlex

This paper uses support vector regressions (SVRs) and Google search data to test whether observing Internet habits can provide insights into trends in tourist arrivals in Barbados. The empirical evidence suggests that Google Trends data may be used to pick up changing patterns and trends in tourist arrivals from the UK and Canada. In the case of the USA, the authors find no evidence to suggest that Google data add any significant information to what can be ‘learned’ from an autoregressive SVR.

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)0.001
metaresearch head score (Gemma)0.013
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.429
Threshold uncertainty score0.854

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.013
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0020.005
Science and technology studies0.0010.001
Scholarly communication0.0030.002
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0160.005

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 head0.052
GPT teacher head0.352
Teacher spread0.300 · 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 designSimulation or modeling
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

Citations30
Published2014
Admission routes1
Has abstractyes

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