Bibliographic record
Abstract
Since a society’s tax system is one of its most basic and essential social institutions, the justice or fairness of this tax system is an important subject for social and political theory, as well as for practical politics. In order to assess the fairness of any particular tax or the tax system as a whole, however, it is essential to consider the purpose of the tax and the tax system in general. While the most obvious purpose of most taxes is to raise revenue to finance public expenditures, taxes are also employed to regulate social and economic behaviour and to shape the distribution of economic resources. For this reason, the concept of tax fairness is necessarily pluralistic, depending on the particular purpose for which the tax is imposed. Where a tax is designed to affect the distribution of economic resources, principles of tax fairness dissolve into broader considerations of distributive justice which determine the manner in which economic resources are fairly distributed and the respective roles of taxes and transfer payments to achieve this distributive goal. Although conceptions of distributive justice differ significantly, widely shared and normatively defensible principles of distributive justice support progressive taxes on income and wealth transfers in order to moderate inequalities that would otherwise prevail in the distribution of income and wealth, as well as the opportunities that result from substantial inheritances.
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 | 0.006 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.008 |
| Scholarly communication | 0.008 | 0.010 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.012 | 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".