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Record W6990489830

Direct Taxation, Tax Treaties and IIAs: Mixed Objectives, Mixed Results

2013· article· en· W6990489830 on OpenAlexaboutno aff

Bibliographic record

VenueeYLS (Yale Law School) · 2013
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicCorporate Taxation and Avoidance
Canadian institutionsnot available
Fundersnot available
KeywordsInternational investmentForeign direct investmentInvestment (military)International lawSustainabilityCustomary international lawCarry (investment)International taxation
DOInot available

Abstract

fetched live from OpenAlex

Tax treaties and international investment agreements (“IIAs”) have much in common. They share the same purpose of facilitating foreign direct investment (“FDI”), and they provide similar legal protections, such as prohibitions of discriminatory treatment of non-nationals and access to binding dispute resolution. Among other objectives, they are intended to reduce risk and create security and predictability, allowing investors to plan and carry out commercially viable activities under the protection of an international legal regime . In this sense, they both contribute to ensuring the sustainability of FDI and the legal regimes that support it. There are other similarities as well. Tax treaties and IIAs have proliferated in tandem during the recent period of intensified globalization. Indeed, they are often negotiated with the same country in close temporal proximity. The same international organizations, the OECD and the UN, have been instrumental in setting standards and drafting models. This chapter examines the interaction of tax treaties and IIAs from a Canadian and international perspective.

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)0.083
metaresearch head score (Gemma)0.149
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 score0.083
Threshold uncertainty score0.440

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0830.149
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0030.002
Bibliometrics0.0130.026
Science and technology studies0.0040.009
Scholarly communication0.0290.017
Open science0.0030.008
Research integrity0.0040.007
Insufficient payload (model declined to judge)0.0160.002

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 head0.013
GPT teacher head0.208
Teacher spread0.195 · 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

Citations0
Published2013
Admission routes1
Has abstractyes

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