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Record W4405171771 · doi:10.70300/jaqm9kmlgdhd

The Transformative and Political Power of Tourism for World Peace: From Past to Present

2024· article· en· W4405171771 on OpenAlexaboutno aff
İbrahim Yorgun, Zornitza Mladenova

Bibliographic record

VenueIzdatel · 2024
Typearticle
Languageen
FieldSocial Sciences
TopicReligious Tourism and Spaces
Canadian institutionsnot available
Fundersnot available
KeywordsTourismPeacebuildingSustainable tourismGeopoliticsPolitical scienceTransformative learningPoliticsDeclarationTourism geographyPower (physics)Sustainable developmentPolitical economySustainabilitySummitPublic administrationSociologyLaw

Abstract

fetched live from OpenAlex

Among the world’s largest industries, tourism has evolved from a cultural exchange mechanism to a tool with significant political implications for peacebuilding. The International Institute for Peace through Tourism (IIPT) was established during the UN's International Year of Peace in 1986, and tourism was positioned as a “Global Peace Industry” with transformative potential. The IIPT’s 1988 Vancouver Conference marked a political milestone, promoting “Sustainable Tourism” and solidifying tourism’s role in international policy to foster understanding, cooperation, and reconciliation. Key documents, such as the Manila Declaration (1985) and the Amman Declaration (2000), framed tourism as a strategic instrument for soft power, diplomatic relations, and conflict resolution. This article underscores tourism’s role in advancing global peace agendas by analyzing how tourism shapes geopolitical perceptions, addresses social justice, and supports UN Sustainable Development Goals. The article concludes by advocating for increased research into tourism’s political influence on peacebuilding while acknowledging the industry’s capacity to bridge divides, promote inclusivity, and support sustainable global relations.

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 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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.410
Threshold uncertainty score0.374

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 head0.014
GPT teacher head0.323
Teacher spread0.309 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
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

Citations1
Published2024
Admission routes1
Has abstractyes

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