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Record W165973404

Fishing Policies and Island Community Development

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

Bibliographic record

VenueMemorial University Research Repository (Memorial University) · 2014
Typearticle
Languageen
FieldSocial Sciences
TopicIsland Studies and Pacific Affairs
Canadian institutionsnot available
Fundersnot available
KeywordsFishingGeographyFisheryRecreationCommercial fishingArchipelagic stateFisheries managementGovernment (linguistics)Environmental resource managementPolitical science
DOInot available

Abstract

fetched live from 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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.767
Threshold uncertainty score0.990

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0110.001
Scholarly communication0.0000.001
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.041
GPT teacher head0.277
Teacher spread0.236 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designNot applicable
Domainnot available
GenreEmpirical

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".

Quick stats

Citations1
Published2014
Admission routes1
Has abstractyes

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