Bibliographic record
Abstract
This book is about the relationship between taxes and democracy. Specifically, it shows how policies to limit local revenue generation at state and local levels in the U.S. have direct and lasting consequences for equity, equality, and fairness. This occurs not simply through shrinking budgets for public goods and services. Tax structures also embed, and reproduce, an implicit social contract between government and citizens, creating path-dependent outcomes that reach into the future and create unintended consequences that are rarely traced back to revenue models. The book begins by showing how legal limitations on taxing decisions by local jurisdictions has led to increasing use of new models for revenue generation that weaken transparency and fiscal accountability, and that subsequently undermine equity. These limits include constitutional and statutory limits on new taxes or rate increases at state and local levels, as well as the use of state powers of preemption to prevent local jurisdictions from increasing revenues using traditional taxes. This has led to a reliance on fines and fees to fund even the most essential public services. The book describes how greater reliance on these less transparent ‘taxes’ undermines fiscal accountability, places unfair burdens on certain groups of citizens, and weakens the connection between governments and citizens. These practices obscure a social contract in which revenue and legitimacy for the state are exchanged for public services offered to all citizens.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.891 | 0.858 |
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; the direct Gemma label and the distilled Codex classifier 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".