Management challenges for the fastest growing marine shipping sector in Arctic Canada: pleasure crafts
Bibliographic record
Abstract
ABSTRACT Changing environmental conditions in the Canadian Arctic are associated with an increase in marine tourism. A substantial decline in the extent of ice coverage in the summer season has resulted in greater accessibility for all categories of ships, and the tourism sector has been quick to respond to new opportunities. This increase in vessel traffic has raised significant issues for management, and particular concerns about the pleasure craft (non-commercial tourism) sector. This paper reports on research aimed at identifying change in the pleasure craft sector in Canadian Arctic waters since 1990; exploring management concerns held by stakeholders regarding changes in the sector; and, providing recommendations for government stakeholders. The paper is based on material gathered through an examination of existing data sources and stakeholder interviews (n= 22). Analysis was aimed at understanding the rapid development of the sector and potential management strategies, including research needs. Analysis reveals a dramatic increase in annual vessel numbers, particularly from 2010 onwards. Management concerns of interviewees relate to implications of this growth in four areas: visitor behaviour; services, facilities and infrastructure; control; and, planning and development. The paper concludes by describing recommendations in the areas of research needs, regulation, and strategic development.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.016 | 0.002 |
| Scholarly communication | 0.006 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".