Newfound Opportunity? The potential impacts of climate change on the tourism industry of western Newfoundland
Bibliographic record
Abstract
The purpose of this research was to assess the potential impact of climate change on the western Newfoundland tourism industry. Western Newfoundland was chosen as it has a variety of recreational activities that attract tourists. \n\tTo this end, a mixed methods approach was deemed most appropriate. It allowed for the use of the qualitative procedures of interviews and document analysis as well as the quantitative procedures of statistical climate modeling. \n\tThe qualitative research demonstrated that there was a desire for further growth in the tourism industry and a general lack of concern for the affects of climate change. The quantitative methods projected that three different recreational and tourism activities studied in this thesis could be altered by climate change. Of the tourism industries examined, snowmobiling was projected to suffer shortened seasons, skiing was projected to see slight losses or to maintain its current season length, and golf was projected to extend its season and increase the number of playable rounds. When the two methods were integrated, there was a gap between the potential changes in the tourism industry and the lack of adaptation plans from the province or the tourism sector. \n\tBased on these findings, a series of recommendations were made to the Newfoundland and Labrador Department of Tourism, Culture and Recreation and various tourism operators. This research will contribute a new perspective to the substantial existing literature on tourism, to the growing research on climate change, and to the essential research on Newfoundland and Labrador.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".