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Record W4210353101 · doi:10.24043/isj.332

Thematic Section: Sustainable development and environmental conservation in the Outermost European Regions

2016· article· en· W4210353101 on OpenAlexaffvenue
Artur Gil

Bibliographic record

VenueIsland Studies Journal · 2016
Typearticle
Languageen
FieldSocial Sciences
TopicIsland Studies and Pacific Affairs
Canadian institutionsUniversity of Prince Edward Island
FundersEuropean Social FundFundação para a Ciência e a Tecnologia
KeywordsSustainabilityEuropean unionSustainable developmentGeographySituatedEnvironmental planningThematic mapPolitical scienceRegional sciencePoliticsEnvironmental resource managementBusinessEcologyEconomicsCartography

Abstract

fetched live from OpenAlex

The Outermost European Regions (OERs) are geographic areas which are part of a European Union Member State, but situated outside continental Europe. All OERs except French Guiana are islands or archipelagos. They face several challenges to full development – remoteness, insularity, terrain and climate constraints, economic dependence and a narrow range of exportable commodities or services. Nevertheless, the European Commission advocates for these regions the assumption of a new paradigm: turning their natural and socioeconomic handicaps into assets. This strategy makes the sustainable development and environmental conservation strategies and policies of OERs especially challenging in scientific, technical and political terms. This Island Studies Journal special section on Sustainable Development and Environmental Conservation in the Outermost European Regions includes five articles that describe, analyse and address directly social-ecological systems’ issues in insular Portuguese and Spanish OERs (Azores and Canaries, respectively). These studies propose novel concepts, strategies and models aiming towards designing and implementing better and more cost-effective sustainability and environmental conservation policies in these remote European regions.

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)0.002
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: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.199
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0020.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.034
GPT teacher head0.270
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 designQualitative
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

Citations9
Published2016
Admission routes2
Has abstractyes

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