The demand for Indigenous tourism: Are there really 'disagreements' on how little we know?
Bibliographic record
Abstract
\n \t\t\tThe paper contrasts results from a survey of mainly consultant-based research on the demand for Indigenous/Aboriginal tourism within the claims made by Ryan and Huyton in a series of articles reporting their analysis undertaken in the Northern Territory. It first introduces a number of claims stemming from Ryan and Huyton's joint work on the topic and then argues that in their depiction of pervious industry enquiries, Ryan and Huyton exaggerate the extent to which research previous to theirs implied an important and generalised growth in demand for such cultural experiences or products. It is argued that their results are fairly similar to those emanating from previous empirical research and gathered in a survey of demand research of Indigenous Tourism in Australia, Canada and New Zealand. The paper concludes by suggesting that methodological difficulties linked with a mismatch between conceptual complexity and methodological simplicity make existing survey-based research on this topic inadequate for the sake of assessing the importance and potential of Indigenous cultural experiences as tourism products, branding themes and as development strategies.\n
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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 | 0.005 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.003 | 0.007 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".