Bibliographic record
Abstract
Everyone wants fair taxation, but what do we mean by it? What impact can taxation have on inequalities? To what extent should the tax system be used to redress them? And do we (truly) want it to do so? Based on extensive new tax law data, and informed by multidisciplinary insights, this book offers answers to what are arguably some of the most challenging questions of our times.Why this book?Over the last two decades, the term “fair taxation” has become ubiquitous in public debate. This is undoubtedly linked to both the growing social concerns about income and wealth inequalities, and the increased awareness of other inequalities, such as in gender and race, and their intersections. Yet, there is also a political economy dimension to this increased popular awareness of “fairness” in tax policy; the term is sufficiently elastic to cover different taxation preferences, simple enough to be intuitively understood by voters, and suitably pro-social to convey a compelling story. From a normative perspective, however, it is precisely this conceptual elasticity that renders the term problematic.Everyone wants fair taxation, but what do we mean by it? What impact can taxation have on inequalities? To what extent should the tax system be used to redress them? And do we (truly) want it to do so?This book offers an answer to these questions. Based on extensive new tax law data – spanning the whole tax system, from tax policy to tax administration, and collated by over 60 academics located in over 30 countries – it presents a novel analytical and conceptual framework of taxation and inequalities, one that is informed not solely by tax law, but also by legal theory, human rights, constitutional and administrative law, as well as by a variety of other disciplines, including public economics, political economy, political science, moral philosophy, sociology, and moral and social psychology. The aim is both to fill a critical scholarship gap and to inform policy, contributing to what is perhaps the most challenging question faced by tax policymakers of our times: How can we build a fair tax system?
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.009 | 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; a candidate call from one teacher head, 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".