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Record W4405458717 · doi:10.1002/epa2.1232

Same money, different impact? The curving effect of European Structural and Investment Funds on EU support in Spain (1990–2019)

2024· article· en· W4405458717 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueEuropean Policy Analysis · 2024
Typearticle
Languageen
FieldSocial Sciences
TopicRegional Development and Policy
Canadian institutionsUniversity of Toronto
FundersErasmus+European CommissionMinisterio de Ciencia, Innovación y Universidades
KeywordsInvestment (military)BusinessEconomicsMonetary economicsPolitical scienceLaw

Abstract

fetched live from OpenAlex

Abstract European Structural and Investment Funds (ESIF) engender European Union (EU) support in generating economic growth, but their effect is conditional on individual European identity and educational background. This study investigates whether the positive impact of ESIF spending on EU attitudes also depends on the alignment of funding with the economic needs of recipient regions. We examine this issue with the Spanish case (1990–2019), employing a unique combined data set of Eurobarometer waves and regional NUTS‐2 economic indicators. Our findings indicate that EU funds manage to decrease Euroscepticism only in laggard regions, which receive the lion's share of funds and allocate them to public goods easily perceived and communicated to the local population. Conversely, the effect of ESIF on transforming attitudes is absent in middle and high‐income regions. The findings suggest a more complicated relationship between ESIF and EU support, which necessitates taking both individual and contextual factors into account.

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.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.524
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0000.000
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 head0.020
GPT teacher head0.334
Teacher spread0.314 · 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