Bibliographic record
Abstract
As alternatives to more general taxes based on broad measures of each taxpayer's economic capacity, benefit taxes and user fees are praised by some for promoting economic efficiency, government accountability and tax fairness, and condemned by others as reactionary, regressive, and distributively unjust. This paper adopts a more even-handed approach to benefit taxes and user fees, regarding these sources of revenue as preferable to general taxation for specific purposes but inferior to general taxes for other purposes. Part II provides a theoretical framework for analyzing benefit taxes and user fees, defining these levies in contrast to general taxes, examining theoretical arguments for and against government reliance on benefit taxes and user fees, considering the appropriate role of benefit taxes and user fees as methods of government finance, and reviewing the manner in which these levies should be designed in order to achieve the purposes for which they are best suited. Part III considers benefit taxes and user fees in practice, surveying the extent to which governments rely on these levies in Ontario and other jurisdictions, and examining the current and potential application of benefit taxes and user fees to finance various categories of government expenditures. Part IV summarizes the argument of the paper and offers general conclusions.
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 | 0.010 | 0.034 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.003 | 0.026 |
| Scholarly communication | 0.012 | 0.015 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.007 | 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".