Bibliographic record
Abstract
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 machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.107 | 0.170 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.012 | 0.016 |
| Scholarly communication | 0.012 | 0.008 |
| Open science | 0.004 | 0.011 |
| Research integrity | 0.008 | 0.012 |
| Insufficient payload (model declined to judge) | 0.008 | 0.008 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".