Bibliographic record
Abstract
Ugo Troiano of the University of Michigan reviews “The Global Debt Crisis: Haunting U. S. and European Federalism”, by Paul E. Peterson and Daniel J. Nadler. The Econlit abstract of this book begins: “Eleven papers, previously presented at a conference held at Harvard University in August 2012, and revised prior to publication, examine the structural flaws in federal systems of government across the globe that have led to economic and political turmoil and present solutions to preserve and restore federal systems that meet the needs of struggling communities. Papers discuss federalism's emerging fiscal crisis; competitive federalism under pressure; whether market discipline can survive in the U.S. federation; putting a price on teacher pensions; structural flaws in the design of public pension plans; past and present high-risk investments by states and localities; between centralization and federalism in the European Union; German federalism at the crossroads; Spanish federalism in crisis; regional identity and fiscal constraints in Spanish federalism; and the resilience of Canadian federalism. Peterson is Henry Lee Shattuck Professor of Government and Director of the Program on Education Policy and Governance at Harvard University, and Senior Fellow at the Hoover Institution. Nadler is a PhD candidate at Harvard University.”
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.
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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.287 | 0.266 |
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".