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Record W2559977893 · doi:10.24043/isj.267

The European Regional Development Fund and Island Regions: An Evaluation of the 2000-06 and 2007-13 Programs

2012· article· en· W2559977893 on OpenAlexaffvenue
Harvey Armstrong, Benito Giordano, Thanasis Kizos, Calum Macleod, Lise Smed Olsen, Ιoannis Spilanis

Bibliographic record

VenueIsland Studies Journal · 2012
Typearticle
Languageen
FieldSocial Sciences
TopicIsland Studies and Pacific Affairs
Canadian institutionsUniversity of Prince Edward Island
FundersEuropean Regional Development FundCohesion FundEuropean Commission
KeywordsRegional policyCohesion (chemistry)Regional scienceAutonomyEuropean commissionGeographyRegional developmentMember statesPolitical scienceEconomic geographyEuropean unionBusinessInternational trade

Abstract

fetched live from OpenAlex

This paper presents results from a regional policy evaluation study conducted for the European Commission. The study examined the impact of the European Regional Development Fund and Cohesion Fund on EU regions with ‘specific geographical characteristics’, namely islands, mountain regions and sparsely populated areas. These types of regions have been attracting increasing EU regional policy attention and their economic development is considered important in helping the EU to attain its important ‘territorial cohesion’ objective. The focus of this paper is on the island regions. Evaluation of island regions in their own right has not been undertaken before by the EU. The study focuses on the 2000-06 and (still on-going) 2007-13 EU regional policy programs. The paper presents the methodology adopted by the study before turning to the main findings concerning the types of policy initiatives adopted in the island regions, and the appropriateness of the policies used for the economic situation faced by the islands. The islands encompassed by the study are all normal sub-national regions of EU member states. Islands with an unusual degree of administrative autonomy (e.g. the Outermost Regions) were excluded.

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)0.007
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: Empirical
Teacher disagreement score0.502
Threshold uncertainty score0.997

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0070.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0040.001
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 head0.151
GPT teacher head0.359
Teacher spread0.208 · 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

Citations19
Published2012
Admission routes2
Has abstractyes

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