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Record W4404317741 · doi:10.2495/st240141

DEVELOPING SUSTAINABLE INDIGENOUS TOURISM BASED ON EDUCATION: A COMPARISON BETWEEN JAPAN AND CANADA

2024· article· en· W4404317741 on OpenAlexaboutno aff
LORENZ POGGENDORF, Takeshi Kurihara, Miho HAMAZAKI

Bibliographic record

VenueWIT transactions on ecology and the environment · 2024
Typearticle
Languageen
FieldSocial Sciences
TopicTourism, Volunteerism, and Development
Canadian institutionsnot available
Fundersnot available
KeywordsIndigenousTourismSustainable tourismSustainable developmentGeographyNatural resource economicsPolitical scienceEconomicsEcologyArchaeology

Abstract

fetched live from OpenAlex

Since the United Nations recognised the rights of indigenous peoples in 2007, indigenous tourism has received increased attention.This paper compares the situation of the indigenous peoples of northern Japan with that of the indigenous peoples of Canada and related indigenous tourism promotion.For this study, in Japan, citizens of both sexes and all age groups were asked about their knowledge of and interest in the Ainu and their culture.For both countries, academic sources and online information were used to research the handling of settler history, relevant legislation, and the current promotion of indigenous peoples.The results so far show that, despite some recent progress in the targeted promotion of indigenous tourism, Japan is still at the beginning compared to Canada and can learn a lot from Canada in terms of historical reappraisal of its indigenous people.

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)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.030
Threshold uncertainty score0.217

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.006
Science and technology studies0.0060.001
Scholarly communication0.0030.001
Open science0.0010.002
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.009
GPT teacher head0.237
Teacher spread0.228 · 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 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

Citations0
Published2024
Admission routes1
Has abstractyes

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