Same money, different impact? The curving effect of European Structural and Investment Funds on EU support in Spain (1990–2019)
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".