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Record W3104949593 · doi:10.1080/24721735.2020.1840104

A code of conduct to guide Indigenous-inspired spas

2020· article· en· W3104949593 on OpenAlexaff
Laura Ell, Joe Pavelka

Bibliographic record

VenueInternational Journal of Spa and Wellness · 2020
Typearticle
Languageen
FieldHealth Professions
TopicGlobal Healthcare and Medical Tourism
Canadian institutionsMount Royal University
Fundersnot available
KeywordsIndigenousTourismSustainabilityBest practiceEmpowermentBusinessStorytellingPublic relationsEcotourismProduct (mathematics)SociologyMarketingPolitical scienceLawNarrativeEcology

Abstract

fetched live from OpenAlex

While best practices are provided for green spas, Indigenous tourism, and ecotourism, there has been an absence of a code of conduct for spas that aim to sustainably integrate culture. The need for guidelines unique to this niche spa sector is critical and timely given the post-COVID-19 demand by travellers seeking out more health-related benefits via wellness holidays. This paper reports on a study of international spa experts and Indigenous healers who incorporate ancient practices into spa experiences to provide benefits to clients through non-exploitative means. The result is a suggested code of conduct as well as a definition for spas offering services or rituals based on or inspired by Indigenous traditions. The code features several themes including risks; honouring culture; product development and training; client experience; as well as local empowerment. The benefits to Indigenous communities include meaningful employment and preserving ancient practices that are at risk of erosion. Benefits to clients include cross-cultural learning through oral history (storytelling) of traditions, and spa options that remedy stress or other health imbalances. The spa sector in turn benefits from guidance as to how to determine if and how cultural elements are suitable to incorporate into their spa menus of offerings.

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 score0.449
Threshold uncertainty score0.279

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 head0.102
GPT teacher head0.463
Teacher spread0.362 · 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

Citations8
Published2020
Admission routes1
Has abstractyes

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