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Record W1965314229 · doi:10.3138/utlj.0717

How to redistribute? A critical examination of mechanisms to promote global wealth redistribution

2014· article· en· W1965314229 on OpenAlexvenueno aff
Ilan Benshalom

Bibliographic record

VenueUniversity of Toronto Law Journal · 2014
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicCorporate Taxation and Avoidance
Canadian institutionsnot available
Fundersnot available
KeywordsRedistribution (election)EconomicsDistributive justicePublic economicsRedistribution of income and wealthDistributive propertyPoliticsLaw and economicsPolitical scienceEconomic JusticeMicroeconomicsLawPublic good

Abstract

fetched live from OpenAlex

The literature on global redistributive justice deals primarily with the important, yet unresolved issues of why global wealth redistribution may be morally justified or beneficial. However, philosophers and economists who address these issues often do not address the question of how such redistribution should take place. This article seeks to rectify this deficiency and argues that, if a certain level of global wealth distribution is morally justified and, more importantly, beneficial, the question of how it should be promoted is far from trivial. In this context, the analysis opens a new discussion of what form of redistributive measures should be adopted in a multistate reality. The article analyses the potential distributive impact of international tax arrangements. It first explains how international tax arrangements, as an indirect method of redistribution, can promote global distributive objectives. It then assesses whether international tax arrangements offer a more effective global wealth redistribution mechanism when compared to other (indirect) alternatives such as fair trade, international labour, and environmental regulation. The article evaluates the strengths and weaknesses of different redistributive arrangements through the lenses of three criteria: the scope of redistribution, the efficiency of redistribution, and the political feasibility of redistribution. It concludes that, under certain plausible circumstances, international tax redistributive efforts would offer a more effective redistributive option compared to other alternatives.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.914
Threshold uncertainty score0.372

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 communication0.0000.001
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 head0.010
GPT teacher head0.208
Teacher spread0.198 · 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 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

Citations12
Published2014
Admission routes1
Has abstractyes

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