Bibliographic record
Abstract
<p>This brief considers the concept of tax justice or fairness in relation to each of these broad goals: the collection of revenues to finance public expenditures, the regulation of social and economic behaviour, and the distribution of economic resources.</p>\n\n<p>With respect to the collection of revenue for public expenditures, it argues that traditional principles of taxation according to benefits received and ability to pay provide useful criteria to assess the justice or fairness of taxes for this purpose. Regarding the regulation of social and economic behaviour, principles of tax fairness necessarily assume a different character, related to the justice of the regulatory goal, the presence of a rational relationship between the tax or tax incentive and the regulatory goal, and the distributional effects produced by the tax or incentive.</p>\n \n<p>Finally, it contends, 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.</p>\n \n<p>Together, the brief concludes, these principles support a mix of taxes, including benefit taxes and user fees, a broad-based consumption tax like a value-added tax, excise taxes on specific goods and services, as well as progressive income and wealth transfer taxes.</p>
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.005 | 0.016 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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; both teacher heads agree on what is shown here.
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".