Putting research into action to manage the risks and opportunities associated with cruise tourism in Arctic Canada
Bibliographic record
Abstract
In this paper we discuss findings from the 'Cruise Tourism in Arctic Canada' (C-TAC) study; a project that focuses on the increased number of cruise ships visiting small communities in the Canadian Arctic between 2005 and 2012. This paper presents a case study of Pond Inlet, Nunavut, one of the communities most visited by cruise vessels in the region. During the summer of 2010, 47 residents were interviewed about the costs and benefits of the industry to the community, with follow up workshops in 2011 and 2012. The paper highlights the development of a visitor code of conduct which was identified as one of the key strategies to minimise risks and maximise opportunities presented by the cruise industry in the community.
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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 | 0.023 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.032 | 0.027 |
| Scholarly communication | 0.023 | 0.006 |
| Open science | 0.005 | 0.012 |
| Research integrity | 0.005 | 0.005 |
| 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".