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, is often made on grounds that such taxes are unfair burdens. This fairness argument can be given a number of further, more-specific formulations. Libertarians, such as Robert Nozick, argue that taxation of income is unfair because it violates individual rights. They invoke an entitlement argument that 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 because tax burdens are only legitimate when they satisfy some form of reciprocity thesis. These arguments are critically assessed here in relation to three recent books – The Cost of Rights, The Myth of Ownership and The Civic Minimum – which explore different arguments often invoked in defence of tax cuts. Themes that raise important questions about taxation and justice are also examined – private property, welfare reform and inheritance. The real challenge facing justice theorists is to take scarcity seriously; thus, I emphasise the shortcomings of simply endorsing a ‘cost-blind’, rights-oriented conception of justice, which 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 | 0.007 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.028 |
| Scholarly communication | 0.010 | 0.010 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.010 | 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".