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Record W4408515994 · doi:10.29173/jaed516

Sustainability, Ethics, and Authenticity in Indigenous Tourism: The Case of Eskasoni Cultural Journeys on Goat Island

2018· article· en· W4408515994 on OpenAlexaffabout
Patrick Maher, Stephanie MacPherson, Mary Beth Doucette, Janice Esther Tulk, Tracy Menge

Bibliographic record

VenueJournal of Aboriginal Economic Development · 2018
Typearticle
Languageen
FieldArts and Humanities
TopicCultural Heritage Management and Preservation
Canadian institutionsCape Breton University
Fundersnot available
KeywordsIndigenousSustainabilityTourismEnvironmental ethicsGeographyEnvironmental planningSociologyPolitical scienceArchaeologyEcologyPhilosophyBiology

Abstract

fetched live from OpenAlex

Cape Breton Island (Nova Scotia, Canada) is well known as an island tourism destination, recognized for its rich natural beauty, as well as cultural and heritage products. Eskasoni First Nation is the largest of the five Mi’kmaw communities located on Unama’ki (Cape Breton Island), and until recently Mi’kmaw communities were not recognized as a significant part of the Cape Breton tourism product mix. Tourism as a means of encouraging economic development is not uncommon internationally, and while tourism growth has been significant on the island, questions remain regarding its ethics and authenticity in relation to community economic development. This paper explores the development of an Indigenous (Mi’kmaw) cultural heritage ecotourism product through a community-led approach. Using Eskasoni Cultural Journeys as a case study, the research presented in this paper questions sustainability through the lens of triple bottom line (TBL) accounting, which looks at economic, social, and environmental aspects of development, as well as ethical business practices, such as authenticity and community well-being.

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)0.002
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: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.805
Threshold uncertainty score0.387

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0230.020
Scholarly communication0.0060.002
Open science0.0010.006
Research integrity0.0020.003
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 head0.048
GPT teacher head0.302
Teacher spread0.254 · 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 designQualitative
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
Published2018
Admission routes2
Has abstractyes

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