Bibliographic record
Abstract
The opening page of this book says that, in recent decades, there has been a ‘substantial increase in the mobility of capital and population between the individual jurisdictions of long‐established federal states (such as Canada, Germany and the USA) and among the formerly independent member countries of the European Union.' It adds that the book will seek to show that the results of this increasing mobility are that fiscal decentralization is essentially beneficial for resource allocation, at least for local public goods, but not for income distribution. It would be apt to start such a book with some data illustrating the ‘substantial increase' in the mobility of population and capital in the places cited. However, the author gives no information for Canada or Germany. For the USA, his data show that inter‐state population mobility has actually fallen since 1970; and while he seems to infer from the falling inter‐regional distribution of incomes in the USA between 1900 and 1990 that capital mobility has increased, this fall might reflect the effects of sustained rather than accelerating migration. For the EU, he accepts that the mobility of labour between member countries is still very low. So the only clear evidence given for increased mobility seems to be that of capital within the EU.
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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.022 | 0.002 |
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".