Undermining Governors: Argentina’s Double-Punishment Federal Spending Strategy
Bibliographic record
Abstract
Abstract Throughout Latin American federations, programmatic welfare spending is increasingly nationally oriented and bureaucratically delivered. By explaining the logic and the effects of combining two types of federal spending, discretionary and non-discretionary, this article uncovers an additional driver that contributes to understanding policymaking and its implementation not only in Argentina, but potentially in other robust federal systems such as Brazil, Canada, and the United States. Using original data on federal infrastructure and programmatic social welfare spending for the twenty-four provinces of Argentina between 2003 and 2015, we provide empirical evidence that both forms of spending penalize opposition districts and more populated urban provinces (regardless of partisan affinity), and thus undercut the ability of key governors to become future presidential challengers. This research suggests that presidents of territorially diverse federations with strong governors can utilize the dual-punishment spending strategy to alter the balance of power, reinforcing the dominance of the center.
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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 | 0.002 | 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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".