The Role of Women in Travel Services in British Columbia Prepared by
Bibliographic record
Abstract
I’d like to take the opportunity to thank the many people who supported my efforts to complete the Masters of Arts in Tourism Management while working full-time. First and foremost, I’d like to thank my family. My husband Matt Westcott has been patience personified as always – even as we planned a wedding in the middle of this Masters-related chaos! My mother Patricia Fraser contributed her keen editor’s eye, my father John Geisler went on several sushi runs during my writing stints, and the whole clan patiently put up with “tourism talk ” over the holidays. A special thanks to my employers and peers at LinkBC: the tourism & hospitality education network, where colleagues Terry Hood and Terry Bubb have been flexible and forgiving as I’ve attempted to juggle the rigours of a research study with communications work, web development, and coordinating the 2009 BC Student Case Competition at the Tourism Industry Conference. I’d also like to thank the hundreds of women working in travel services who took time out of their busy schedules to answer my survey; and the industry leaders who served as interview participants. Your involvement in this study has demonstrated that people working in
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.032 | 0.006 |
| Scholarly communication | 0.007 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.021 | 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".