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Record W1513944404

The Dubious Legal Pedigree of IGAs (and Why it Matters)

2013· article· en· W1513944404 on OpenAlexaff
Allison Christians

Bibliographic record

VenueSSRN Electronic Journal · 2013
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicCorporate Taxation and Avoidance
Canadian institutionsMcGill University
Fundersnot available
KeywordsTreatyEnforcementTaxpayerStatuteTreasuryStatutory lawCredibilityPolitical scienceLaw and economicsLawDelegationCompliance (psychology)BusinessEconomics
DOInot available

Abstract

fetched live from OpenAlex

When Congress enacted the Foreign Account Tax Compliance Act in 2010, it made no mention of any internationally-agreed alternative to its enforcement, and Congress has made no authorization since then for the president to override FATCA’s statutory provisions by international agreement. Yet due to difficulties in implementing FATCA, Treasury has entered into several ‘‘intergovernmental’’ agreements (IGAs) to essentially bypass the hurdles, even going so far as to draft model IGAs with the intent of streamlining their enactment globally. This column examines the nature of these agreements and concludes that their legal pedigree is tenuous as a constitutional matter. It argues that this pedigree implicates the rule of law in two ways: first, if the IGAs are not good law in the U.S., then FATCA partners incur the risk of penalties should the statute they seek to override apply in default. Second, and more fundamentally, the IGAs violate the rule of law by ignoring established procedural requirements for binding the US internationally. This undermines the legal system in the US domestically as well as hurting U.S. credibility in the international community. The column concludes by arguing that there is no benefit to be had in skirting the normal legal process for concluding international agreements in the rush to implement FATCA. Instead of jeopardizing the important and complex project of global tax compliance with such a legally dubious procedure, the obvious and straightforward approach to making FATCA work internationally is to follow the normal treaty-making procedure, time-tested through 100 years of US tax treaty-making history.

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)0.038
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.030
Threshold uncertainty score0.068

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0130.038
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0070.030
Scholarly communication0.0120.014
Open science0.0010.004
Research integrity0.0040.009
Insufficient payload (model declined to judge)0.0050.001

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 head0.007
GPT teacher head0.190
Teacher spread0.184 · 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 designNot applicable
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

Citations3
Published2013
Admission routes1
Has abstractyes

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