Generation Gap – Balancing Cultural and Economic Issues for Indigenous Tourism
Bibliographic record
Abstract
Summary Technology plays a critical role in supporting tourism development. Technologies such as data analytics and social media tools can enhance tourism by providing customers with increased accessibility, information, and personalisation. Further research should examine Indigenous tourism operations and their interdependence with technology. This study explores the use of technology to enhance tourism development in Indigenous communities. It explores how technology enhances tourism development in a small, remote Indigenous community of Fort Liard, Canada (situated in the Dehcho Region in the south-west corner of the North-west Territories). Data was collected through observations and semi-structured interviews with key informants and subsequently analysed. The study confirms that technology use in tourism is beneficial to Indigenous communities. Technology is a channel for understanding traditional cultural ways of life and motivates Indigenous tourism development. Digital platforms and e-commerce amplify the global visibility of Indigenous arts. The platforms enable tourists to purchase directly from Indigenous artists, ensuring that profits go directly to them. Additionally, technology is leveraged to enrich cultural knowledge. Thus, the way Indigenous people within the tourism sector utilise technology offers direct benefits for the sustainable development of Indigenous communities. Information © The Authors 2025
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.007 | 0.006 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.017 | 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".