Another legacy for Canada's 2010 Olympic and Paralympic Winter Games: applying an ethical lens to the post-games' sled dog cull
Bibliographic record
Abstract
In the spring of 2010, approximately two months after the conclusion of the 2010 Olympic and Paralympic Winter Games (Winter Olympics) held in British Columbia, Canada, approximately 100 sled dogs were culled in what the media quickly dubbed a ‘massacre’. A sled dog tour company had overestimated the tourism draw and demand of the Winter Olympics and was consequently ‘over-stocked’ with sled dogs. This paper takes a case study approach to examine the sled dog culling through the ethical lenses of utilitarianism, rights and ecofeminist theory. The application of these three perspectives provides evidence that the behaviour of both the sled dog tour company and the employee who carried out the killings was morally wrong. The implications of this case study are far reaching; the tourism industry can no longer afford to ignore the ethical elements that relate to the industry's use and, frequently, abuse of non-human animals. The recent interest by tourism scholars in the ethical aspects of the industry's use of non-human animals is timely. This under-researched topic of study will benefit from more scholarly interest and study, particularly as it relates to the effectiveness of codes of ethics and conduct, corporate social responsibility programmes, and the practicality and value of incorporating ethical education and training into these various programmes.
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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 | 0.006 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.027 | 0.041 |
| Scholarly communication | 0.013 | 0.003 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.003 | 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".