Bibliographic record
Abstract
Distributive justice concerns the fair distribution of the benefits and burdens of social cooperation. Opposition to higher rates of taxation, or even existing levels of taxation, are often made on grounds that such taxes are unfair burdens. This fairness argument can be given a number of further, more specific, formulations. Libertarians like Robert Nozick, for example, argue that taxation of income is unfair because it violates individual rights. Libertarians invoke an entitlement argument which presumes that the appropriate baseline of property rights is pretax income. Others take issue with specific policies that are supported by taxation, such as welfare provisions, and argue that welfare reform is necessary as tax burdens are only legitimate when they satisfy some form of reciprocity thesis. In this review article, I critically assess these arguments. The recent publication of The Cost of Rights: Why Liberty Depends on Taxes, The Myth of Ownership: Taxes and Justice, and The Civic Minimum: On the Rights and Obligations of Economic Citizenship help shed some light on each of these different arguments that are often invoked in defence of tax cuts. These three books are a welcome addition to debates about distributive justice as they help bridge the gap between normative theory and public policy. In addition to raising doubts about the arguments that taxation is unfair, I examine themes that raise important questions about taxation and justice-private property, welfare reform and inheritance. An examination of these themes should make it clear that the real challenge facing justice-theorists is to take scarcity seriously and, thus, I emphasis the shortcomings of simply endorsing a 'cost-blind' rights-oriented conception of justice. Such a conception of justice currently dominates debates in normative political theory.
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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 | 0.005 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.005 | 0.024 |
| Scholarly communication | 0.009 | 0.011 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.014 | 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".