Sharing Identity through Indigenous Tourism: Osoyoos Indian Band’s Nk’Mip Desert Cultural Centre
Bibliographic record
Abstract
Expressing a visual, indigenous identity in tourism can be a balancing act between maintaining a level of recognition and familiarity that mirrors the expectations of the public imagination and conveying a representation that is locally meaningful and emblematic to hosts. This article addresses this issue through the example of the Nk’Mip Desert Cultural Centre, owned and operated by the Osoyoos Indian Band (OIB) of British Columbia. Semiotic and visual analyses were used to explain the messages about OIB identity that the Centre communicates and to provide a framework to discuss three main issues in tourism discourse: control, hybridity and authenticity. Abstract: L’expression visuelle de l’identite autochtone dans le secteur touristique s’avere parfois un exercice d’equilibre. Il faut maintenir un niveau de reconnaissance et de familiarite qui repond aux attentes et aux imaginaires du public, mais aussi projeter une representation qui est localement signifiante et caracteristique des hotes. Cet article s’interesse a ce sujet au travers de l’exemple du Nk’Mip Desert Cultural Centre de la bande indienne Osoyoos (BIO) de la Colombie-Britannique. Les messages concernant l’identite de la BIO que vehicule le centre sont etudies a partir d’analyses semiotiques et visuelles qui offrent egalement un cadre pour considerer trois grands enjeux propres au discours sur le tourisme : le controle, l’hybridite et l’authenticite.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.012 | 0.013 |
| Scholarly communication | 0.005 | 0.001 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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.
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".