Last chance tourism: a decade review of a case study on Churchill, Manitoba’s polar bear viewing industry
Bibliographic record
Abstract
For over 50 years, Churchill, Manitoba has provided visitors an opportunity to see polar bears in their natural environment. Over the same time period, an increase in temperatures and related reductions in sea ice has negatively impacted the health of polar bears in the Western Hudson Bay. In 2008, the term ‘last chance tourism’ was coined, linking the demand to travel to the North with a desire to see these animals ‘before they are gone’. This creates a paradox as tourists require energy-intensive modes of transportation to reach the Arctic, thereby contributing to greenhouse gas emissions. This paper compares the polar bear viewing industry’s total greenhouse gas contribution and tourists’ knowledge about climate change with results from a 2008 study and discusses any changes over the last ten years. During the 2018 polar bear viewing season, greenhouse gas emissions were estimated to be 23,017 t/CO2, an increase from 2008. The results also indicated that although most tourists believe climate change is happening, fewer associate air travel to this — a similar finding identified ten years ago. Findings from this research show that consumption patterns have not changed despite a growing awareness of climate change and its impacts.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.018 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".