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Record W2736073378

Assessing BEPS: Origins, Standards, and Responses

2017· article· en· W2736073378 on OpenAlexaff
Allison Christians, Stephen E. Shay

Bibliographic record

VenueSSRN Electronic Journal · 2017
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicCorporate Taxation and Avoidance
Canadian institutionsMcGill University
Fundersnot available
KeywordsBase erosion and profit shiftingInterimPolitical scienceCorporate governanceHarmonizationBusinessAccountingPublic administrationInternational taxationTax reformLawFinance
DOInot available

Abstract

fetched live from OpenAlex

The G20/OECD’s multi-year campaign to combat base erosion and profit shifting (BEPS) marks a critical step in the evolution of the international tax regime and the roles of institutions that guide it. This General Report for Subject 1, IFA Congress 2017, provides a snapshot of the outcomes of the BEPS project by comparing national responses to key mandates, recommendations and best practices through the end of October, 2016 based on National Reports representing the perspectives of 48 countries. These National Reports reveal that the impact of the BEPS initiative on a particular country corresponds to at least three key factors, namely: (1) the extent to which domestic law is already in substantial compliance with BEPS outcomes; (2) the degree to which implementation of BEPS outcomes appears capable of delivering positive revenue or economic results, or both, relative to a country’s experiences and perceptions prior to BEPS; and (3) the type and degree of involvement of a country in the formative stages of the initiative preceding the release of the final BEPS action plans. As BEPS continues to unfold, it is difficult to gauge the full extent to which countries in fact will adhere or defect from the rules. However, the BEPS project has witnessed the transition of global tax governance from the OECD countries exclusively to global fora. This leaves open questions regarding agenda-setting for international tax policy going forward. As we conclude this interim snapshot of the origins, standards, and responses to BEPS to date, we look to future IFA congresses for answers to these questions and a final assessment of the BEPS project.

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)0.077
metaresearch head score (Gemma)0.179
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.077
Threshold uncertainty score0.410

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0770.179
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0050.005
Science and technology studies0.0040.004
Scholarly communication0.0080.006
Open science0.0020.011
Research integrity0.0020.005
Insufficient payload (model declined to judge)0.0060.002

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 head0.024
GPT teacher head0.297
Teacher spread0.273 · 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 designObservational
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

Citations16
Published2017
Admission routes1
Has abstractyes

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