Bibliographic record
Abstract
Melville L. McMillan of University of Alberta reviews “Fiscal Federalism: Principles and Practices of Multiorder Governance” by Robin Boadway, Anwar Shah,. The EconLit Abstract of the reviewed work begins “Presents an account of the principles and practices of fiscal federalism based on the currently accepted theoretical framework and best practices. Discusses an introduction to federalism and the role of governments in federal economies; the decentralization of government authority; expenditure assignment; revenue assignment; natural resources ownership and management in a federal system; local governance in theory; local governance in practice; revenue sharing; the principles of intergovernmental transfers; the practice of intergovernmental transfers; finance and provision of health and education; finance and provision of infrastructure; poverty alleviation in federations; fiscal federalism and macroeconomic governance; interregional competition and policies for regional cohesion and convergence; decentralized governance and corruption; and adapting to a changing world. Boadway is David Chadwick Smith Chair in Economics at Queen’s University. Shah is Lead Economist and Program Leader of the Public Sector Governance Program at the World Bank Institute. Index.”
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.352 | 0.296 |
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".