Bibliographic record
Abstract
Abstract This chapter, together with chs. 2 and 10, approaches the question of development funding in a theoretical way, rather than by examining individual proposals for sources. One purpose of the book is to bring to bear on this accumulated knowledge in the field of national public finance, and more generally public economics. This chapter looks at the lessons to be learned from the fiscal federalism literature. It highlights some of the similarities and some of the differences between fiscal institutions in federations and those that might apply in a global setting, and draws a number of conclusions about sources of new revenues for development, dealing specifically with taxes on nations, taxes on global externalities, and taxes on internationally mobile tax bases. The three main sections of the chapter look at: revenue‐raising in a federal setting – assignment of revenue‐raising authority, intergovernmental transfers, cooperative behaviour by subnational governments, and freeriding by subnational governments; revenue‐raising in federations with no central government – non‐cooperative and cooperative subnational redistribution; and the implications for global revenue sources – taxes on nations (a global equalisation scheme), taxes on international externalities, and taxes on internationally mobile tax bases.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 0.001 |
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".