Regional redistribution and stabilization by the center in Canada, France, the UK and the USA reassessment and new tests
Bibliographic record
Abstract
This paper re-examines earlier estimates of regional redistribution and stabilization through the central government budget in the US, and produces new estimates of this redistribution and stabilization in the US, Canada, France and the UK. The new estimates rest on panel data econometrics and an adherence to certain accounting principles that have occasionally been violated in the past. As a result of the accounting, the peak estimates for the US and Canada in the earlier literature are never attained. Regional stabilization of personal income through the central government budget emerges as close to 20% in the US, France and the UK, but only 10 to 14% in Canada. In case of gross product instead of personal income accounting, regional stabilization is closer to 10% in the US. As regards France and the UK, the use of panel data econometrics proves essential.
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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 | 0.001 | 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 | 0.000 | 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) | 0.000 | 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".