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Record W2919684572 · doi:10.24043/isj.368

Civil participation between private and public spheres: the island sphere and fishing communities in the Azores archipelago.

2016· article· en· W2919684572 on OpenAlexaffvenue
Alison Neilson, Rita São Marcos

Bibliographic record

VenueIsland Studies Journal · 2016
Typearticle
Languageen
FieldSocial Sciences
TopicIsland Studies and Pacific Affairs
Canadian institutionsUniversity of Prince Edward Island
FundersFundação para a Ciência e a Tecnologia
KeywordsArchipelagoPoliticsCommonsSociologyCitizen journalismFishingCivil societyCorporate governancePolitical scienceEnvironmental ethicsGeographyLawArchaeologyBusiness

Abstract

fetched live from OpenAlex

This paper discusses civic participation with reference to fishing communities in the Azores archipelago, Portugal. We explore how concepts and political processes actively exclude people, and how researchers could dig deeper to find opportunities to build from diverse cultural practices of participation. Specifically, we describe examples of efforts towards participatory sustainable development as well as introduce a centuries-old highly participatory practice of sharing food. The rituals of the Cult of the Holy Spirit, based on sharing and justice, are an example of strong civic engagement rich with possibility from which to build alternatives to current forms of participation for fisheries governance. We suggest that islands offer understandings of human social interactions in ways that larger landmasses might not. This is a call for reflection on images underlying our understandings of participation and governing the sea commons, and looking more closely at islanders and their long held practices.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.096
Threshold uncertainty score0.192

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0070.007
Scholarly communication0.0040.003
Open science0.0010.006
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.081
GPT teacher head0.333
Teacher spread0.252 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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

Citations5
Published2016
Admission routes2
Has abstractyes

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