Investigating the benefits and impacts of tourism development for the Tl'azt'en First Nation in northern British Columbia.
Bibliographic record
Abstract
This thesis is the result of the research that was completed with Tl'azt'en Nation. Tl'azt'en Nation partnered with the University of Northern British Columbia to form a Community University Research Alliance (CURA). This CURA project funded by the Social Science and Humanities Research Council of Canada (SSHRCC) allowed me to look at the benefits (positive) and impacts (negative) of tourism development for Tl'azt'en Nation and the issues surrounding the development of tourism in traditional Tl'azt'en territory. The research for this project was developed with the assistance of the Tl'azt'en community, and various social science techniques were employed to gather data with these community members. Techniques included: tourism presentations, workshops and semi-structured interviews. Data gathered from the community members led to the discovery of new themes and community feelings that were unexpected at the outset of the research. The research showed that Tl'azt'en Nation is in favor of tourism development, yet additional elements also emerged throughout the community members' information sharing sessions. These alternate, unexpected elements showed that Tl'azt'en Nation community members are concerned about their culture, youth and elders. The work done in the community revealed that Tl'azt'en Nation is willing to develop tourism, but with the wish to protect and enhance their culture, youth and elders while doing so. --Leaf ii.
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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 | 0.008 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".