Bibliographic record
Abstract
The purpose of this article is to examine the fiscal flows produced by the actions of the Spanish central public sector concerned with Catalonia and their main determinants. We also compare the Catalan fiscal balance with that of rich territories in other countries (Australia, Belgium, Canada, the USA, Spain and Italy). To this aim the article examines, first, whether the methodology used in Catalonia meets international standards; second, whether the Catalan fiscal deficit is indeed among the largest in a sample of territories analysed; third, which are its main determinants. The methodology for calculating fiscal balances is found to be fairly standardised internationally, revealing that Catalonia has a structural fiscal deficit of between 6 and 8 percent of GDP, according to whether the benefit or the flow approach is applied. These figures are a consequence, to a large extent, of the Spanish system of financing the Autonomous Communities. Finally, we briefly discuss whether the fiscal flows can justify, at least partially, the current secessionist process of Catalonia.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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; both teacher heads agree on what is shown here.
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".