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Record W4242053850 · doi:10.1257/jel.47.1.171.r10

Book Reviews

2009· article· en· W4242053850 on OpenAlexaboutno aff

Bibliographic record

VenueJournal of Economic Literature · 2009
Typearticle
Languageen
FieldSocial Sciences
TopicSocial Policy and Reform Studies
Canadian institutionsnot available
Fundersnot available
KeywordsPoliticsContext (archaeology)Consumption (sociology)Tax reformGovernment (linguistics)Goods and servicesPolitical scienceConsumption taxValue (mathematics)EconLitValue-added taxPublic administrationEconomicsEconomyPublic economicsIndirect taxSociologyGeographyLawSocial science

Abstract

fetched live from OpenAlex

Isaac W. Martin of University of California, San Diego reviews “Taxing Reforms: The Politics of the Consumption Tax in Japan, the United States, Canada and Australia” by Richard Eccleston,. The EconLit Abstract of the reviewed work begins “Explores the politics of consumption tax reform in the four countries where the political resistance to such policies has been most acute--Australia, Canada, Japan, and the United States. Provides an overview of the contemporary literature on institutional and policy change and identifies a number of processes and mechanisms likely to be associated with comprehensive tax reform. Presents the empirical context for the book’s case studies and describes the rise and proliferation of value-added taxes over the course of the twentieth century. Describes the politics of consumption tax reform in Australia between the early 1970s and 2000. Considers the politics of introducing a national goods and services tax in Canada. Assesses the most notable exception to the trend toward implementing national level value added taxes among advanced industrial nations with an American case study. Identifies a number of occasions on which U.S. policymakers gave serious consideration to the introduction of a national value added tax. Eccleston is Senior Lecturer in the Department of Government at the University of Tasmania. Index.”

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.316
Threshold uncertainty score0.170

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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)0.0000.000

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.

Opus teacher head0.022
GPT teacher head0.347
Teacher spread0.326 · 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; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreReview

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
Published2009
Admission routes1
Has abstractyes

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