A Study of Local Government Engagement in Tourism in British Columbia
Bibliographic record
Abstract
“We are so often caught up in our destination that we forget to appreciate the journey, especially the goodness of the people we meet on the way ” (n.a., 2009). Throughout the entire journey of this Master’s degree, I have been blessed with the support and love of many, and it is here that I would like to thank the many people who supported my efforts to complete the Masters of Arts in Tourism Management while working full-time; I could not have done this without you. To my MATM cohort, thanks for sharing in the experience and creating such a strong sense of community, I am very blessed to have worked with all of you. To the MATM faculty and my advisor, Dr. Brian White for your endless words of encouragement, direction, and support. Thank you all for making this such a fun and memorable experience! I would also like to thank the many local governments across B.C. who took time out of their busy schedules to answer my survey. Without your time, insights, and participation, I would not have been able to complete this project. Thanks to my employer and peers at the Ministry of Tourism, Culture and the Arts for your flexibility and continued encouragement.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.005 |
| Science and technology studies | 0.015 | 0.003 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| 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".