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Record W2945207185 · doi:10.1177/1354816619851404

Armed conflict, military expenditure and international tourism

2019· article· en· W2945207185 on OpenAlex
Usman Khalid, Luke Emeka Okafor, Nusrate Aziz

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.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueTourism Economics · 2019
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicDefense, Military, and Policy Studies
Canadian institutionsAlgoma University
Fundersnot available
KeywordsTourismAttractivenessPanel dataArmed conflictMiddle EastEconomicsDevelopment economicsInternational tradeGeographyPolitical science

Abstract

fetched live from OpenAlex

This article uses a gravity model to explore whether military spending has any moderating effect on the link between armed conflict and international tourist flows. The data set consists of a panel of 188 countries over the period 1995 to 2015. We show that the moderating effect of military spending depends on the levels of relative military spending as well as geographical location. Specifically, in the presence of armed conflict, ‘moderate’ level of relative military spending helps to promote the international tourism attractiveness of destination countries, whereas ‘high’ level of relative military spending cannot reverse the negative impact of armed conflict, it rather fuels the problem. In general, countries in regions such as Southeast Asia that allocate ‘moderate’ amount of resources for security attract more international tourists relative to countries in regions, such as the Middle East and North Africa, that spend a larger share of GDP on security.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.552
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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)0.0010.002

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 head0.028
GPT teacher head0.230
Teacher spread0.202 · 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