Understanding patterns of pleasure craft tourism in the Canadian Arctic and implications for safety management
Notice bibliographique
Résumé
This study establishes an understanding of pleasure craft tourism patterns in the \nCanadian Arctic from 1990-2013 with a focus on the implications for safety. Two \nspecific objectives were fulfilled: 1. to develop an understanding of the pleasure craft \nvessel traffic and pleasure craft travel patterns, and 2. to develop an understanding of \nincidents, close-calls, and safety issues. \nPleasure craft tourism in the Canadian Arctic is a relatively new industry, \nalthough it is now the fastest growing marine sector. There is a lack of information on \nthese small vessels compared to larger expedition cruise ships that have been the focus \nof research and management concerns. The increase in pleasure craft traffic in the \nregion should raise concern about traffic patterns and safety of these tourists because they are traveling in a region with limited infrastructure, services, and search and \nrescue. Other issues that need examination are behaviour, monitoring, and control of \npleasure craft vessels, indicating the need for insight into vessel numbers, spatial \npatterns, and vessel preparedness. \nA literature review was conducted to identify the current state of pleasure craft \ntourism in the Canadian Arctic. This included identifying patterns of vessel traffic, \ndefining pleasure craft and the management context, as well as the management context \nof Antarctica and the European Arctic. The literature review concludes with the \nknowledge gaps related to pleasure craft tourism that drive this study. \nThis research takes a quantitative approach to understanding pleasure craft \nvessels in the Canadian Arctic. This study uses two main sources of data: the Pleasure \nCraft Dataset, developed specifically for use in this project; and, a database of Internet \nweb logs (Blog File) gathered for this research. The Pleasure Craft Dataset is comprised \nof information on pleasure crafts extracted from the NORDREG database (for the \npurposes of this research called the NORDREG pleasure craft data), a publicly available \ndatabase collected by the Canadian Coast Guard, and data on additional vessels found \nthrough a literature review and Internet searches. The first phase of this study involved \nthe analysis of the Pleasure Craft Dataset to explore spatial and temporal patterns. The \nsecond phase of this study used content analysis on blogs with material focusing on the \nexperiences of pleasure craft travelers in the Canadian Arctic. \nThe results show an increase in pleasure craft tourism in the Canadian Arctic, \nand a concentration of vessels and vessels days spent in the Northwest Passage while \ndemonstrating that not all vessels are reporting to NORDREG. Further, vessels days are \nnow greater on a per vessel basis than in the past. The results also indicate an increasing \nnumber in pleasure craft travelers overall and the advent of larger pleasure craft vessels \nto the region. Blog analysis was able to provide insight into pleasure craft travelers and \ntheir vessels, including aspects such as: sites visited, behaviour of travelers, and \ninteractions with the environment. The increase in vessel numbers, larger pleasure craft \nvessels in the region, and the spatial extension of vessel presence presents issues for management in the Canadian Arctic regarding growing pressure on infrastructure, \nservices, and search and rescue. \nThere is a need for further research on pleasure craft tourism in Arctic Canada. \nAdditional research should contribute to this sector of marine tourism by focusing on \nunderstanding management implications related to safety, insurance, behaviour \ncontrols and monitoring. There is also a need for research into pleasure craft tourism \nexperiences, the views of community members on the sector, and the role of individuals \nwho provide support to pleasure craft tourism formally and informally. There is also a \nneed for policies and guidelines to aid pleasure craft travelers, and quite possibly a \nneed for mandatory pleasure craft reporting to ensure appropriate monitoring and \nsupport.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,002 | 0,004 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,001 |
| Bibliométrie | 0,008 | 0,014 |
| Études des sciences et des technologies | 0,004 | 0,002 |
| Communication savante | 0,005 | 0,002 |
| Science ouverte | 0,001 | 0,002 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,004 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».