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Enregistrement W165973404

Fishing Policies and Island Community Development

2014· article· en· W165973404 sur OpenAlexaboutno aff
Emily Thomas, Kelly Vodden, Ratana Chuenpagdee, Maureen Woodrow

Notice bibliographique

RevueMemorial University Research Repository (Memorial University) · 2014
Typearticle
Langueen
DomaineSocial Sciences
ThématiqueIsland Studies and Pacific Affairs
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésFishingGeographyFisheryRecreationCommercial fishingArchipelagic stateFisheries managementGovernment (linguistics)Environmental resource managementPolitical science
DOInon disponible

Résumé

récupéré en direct d'OpenAlex

Fisheries have a long history of being the economic backbone of the coastal and island
\ncommunities that dot the coastline of Newfoundland and Labrador. The policies and
\nmanagement structure that guide resource use in the province have had, and will continue to
\nhave, an impact on those communities. The Fishing Policies and Island Community
\nDevelopment project set out to examine these impacts in two areas (Anchor Point and Fogo and
\nChange Islands) and also to explore how these communities have responded to and even
\ninfluenced these policies, management structures and impacts. Brief comparisons are also made
\nto findings from a related research project in three island fishing communities in Maine.
\nThe study drew from bodies of literature in Archipelagic Island Studies and Comanagement.
\nThe research involved secondary data and document review as well as 28
\ninterviews conducted with government and community representatives in 2012. A series of
\nknowledge mobilization activities have also been undertaken, including a project web page,
\npresentations and feedback on initial results obtained at a fall 2012 symposium dedicated to
\nfisheries and community research on the west coast of Newfoundland, and a forum scheduled for
\nFogo Island and Change Islands in May 2014.
\nThe collapse of the groundfishery in the 1990’s, coupled with the rise of snow crab and
\nshrimp fisheries, has influenced how communities respond to changes in the fishery. Policies of
\nimportance to communities have included those related to licensing, quotas and other methods of
\ncontrolling and limiting catch, rationalization, processing and marketing and recreational/food
\nfisheries. The two regions focused upon in this study, Fogo Island/Change Islands and Anchor
\nPoint and area, have been active players in influencing how fisheries policies and management
\ndecisions and other measures impact their communities. Fogo Island and Change Islands share
\nthe presence of the Fogo Island Co-operative, Ltd., for example. The Fogo Island Co-operative
\noperates facilities on Fogo Island and has also operated the community-owned fish plant on
\nChange Islands. The Co-operative is joined by the more recent development of Shorefast
\nFoundation, which plays a role in promoting stewardship, experimentation with alternative gear
\ntypes, and development of new high value markets for island seafood products, particularly cod.
\nAnchor Point shrimp harvesters, in addition to the rest of the 4R fleet, have participated in a
\nvoluntary late start to their fishery, delaying the opening of their season to May 1st from April 1st.
\nEntering new fisheries, vessel upgrades and travelling for employment in other sectors have been
\nadditional strategies employed. Community quotas were also suggested in both regions. We
\nfound that these communities, while threatened by changes in the fishery and Newfoundland
\neconomy more broadly, have innovative ways of responding to changes in two key ways: 1)
\nworking within the existing management structure (as the 4R harvesters did) to influence local
\napplications of fisheries policy, and 2) creating news way to buy, sell, and market their catch (as
\nthe Fogo Island Co-operative and Shorefast Foundation have done). Local governments and
\ncommunity organizations have also lobbied for policy change but the impacts of these efforts are
\nless evident in a system that remains largely driven by centralized decision-makers.

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 distillée sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.

score de la tête « metaresearch » (Codex)0,003
score de la tête « metaresearch » (Gemma)0,000
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesÉtudes des sciences et des technologies
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Sans objet · Signal consensuel: aucune
GenreSignal candidat: Empirique · Signal consensuel: aucune
Score de désaccord entre enseignants0,767
Score d'incertitude au seuil0,990

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0030,000
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0010,001
Études des sciences et des technologies0,0110,001
Communication savante0,0000,001
Science ouverte0,0010,001
Intégrité de la recherche0,0000,001
Charge utile insuffisante (le modèle a refusé de juger)0,0000,000

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.

Tête enseignante Opus0,041
Tête enseignante GPT0,277
Écart entre enseignants0,236 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_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 découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.

Devis d'étudeSans objet
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations1
Publié2014
Routes d'admission1
Résumé présentoui

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