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Record W2906621959

Tax Competition and the Ethics of Burden Sharing

2018· article· en· W2906621959 on OpenAlexaff
Ivan Ozai

Bibliographic record

VenueFordham international law journal · 2018
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicTaxation and Legal Issues
Canadian institutionsYork University
Fundersnot available
KeywordsTax competitionTax reformCompetition (biology)Public economicsEconomicsInternational economicsIndirect taxValue-added taxNegotiationBusinessPolitical science
DOInot available

Abstract

fetched live from OpenAlex

Tax scholars have long suggested that tax competition should be mitigated because it reduces the collective revenues of countries, impairs their ability to redistribute wealth, and produces more regressive tax systems.Likewise, international organizations such as the Organization for Economic Co-operation and Development ("OECD") and the European Union increasingly move towards designing a global framework aimed at reducing tax avoidance and mitigating tax competition.However, there is no comprehensive discussion on the costs that would arise from institutional reform designed to tackle tax competition.To the extent that any change in the international tax order will benefit some countries but also harm others, an ethical analysis of tax competition should include an examination of how to distribute the losses resulting from overall institutional reform.As international policy decisions tend to reproduce the present imbalance of the global power, the lack of an explicit discussion on how to share the costs arising from an institutional change might result in countries with less negotiating power bearing most of these costs.

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 imitation

Not 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.

metaresearch head score (Codex)0.013
metaresearch head score (Gemma)0.015
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.013
Threshold uncertainty score0.071

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0130.015
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0080.047
Scholarly communication0.0090.008
Open science0.0010.005
Research integrity0.0080.008
Insufficient payload (model declined to judge)0.0030.001

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 head0.026
GPT teacher head0.282
Teacher spread0.256 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
Domainnot available
GenreEmpirical

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

Citations4
Published2018
Admission routes1
Has abstractyes

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