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Record W4362605963 · doi:10.51952/9781529215588.fm001

Front Matter

2023· paratext· en· W4362605963 on OpenAlexfundno aff
Carrie Manning

Bibliographic record

VenueBristol University Press eBooks · 2023
Typeparatext
Languageen
FieldSocial Sciences
TopicLocal Government Finance and Decentralization
Canadian institutionsnot available
FundersHome OfficeUniversity of OttawaGeorgia State University
KeywordsFront (military)GeologyOceanography

Abstract

fetched live from OpenAlex

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.020
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.023

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.

Opus teacher head0.027
GPT teacher head0.250
Teacher spread0.223 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; both teacher heads agree on what is shown here.

Study designNot applicable
Domainnot available
GenreOther

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".

Quick stats

Citations0
Published2023
Admission routes1
Has abstractyes

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