Rules, Politics and the Normative Analysis of Taxation
Bibliographic record
Abstract
Taxation has been a much-discussed subject in the literature on economics and in writings on the role and meaning of the state. Over the centuries, many authors have put forward views of what qualifies as “good ” taxation and what constitutes undesirable tax policy. Consensus on these issues has changed over time, depending on historical circumstances and prevailing modes of economic thinking. In this chapter, we look at analytical views that enjoy broad acceptance in the current literature on taxation. We call these views “rules ” or “norms ” of analysis. They represent patterns of thinking that have wide currency or that have become codified in the literature. The chapter describes eight of the most important rules or norms and then critically examines their validity in a framework that makes explicit allowance for collective choice. Our critique Taxation has been a much-discussed subject in the literature on economics and in writings on the role and meaning of the state. Over the centuries, many authors have put forward views of what qualifies as “good ” taxation and what constitutes undesirable tax
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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 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".