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Record W4405557110 · doi:10.1007/978-3-031-69793-7_1

Introduction to Part I

2024· book-chapter· en· W4405557110 on OpenAlexaff
Allison Christians

Bibliographic record

VenueEmerging globalities and civilizational perspectives · 2024
Typebook-chapter
Languageen
FieldBusiness, Management and Accounting
TopicCorporate Taxation and Avoidance
Canadian institutionsMcGill University
Fundersnot available
KeywordsComputer science

Abstract

fetched live from OpenAlex

Abstract The Chapters in this Part introduce readers to the multifaceted and interconnected governance aspects of tax, trade, and investment regimes. Navigating these regimes, states find themselves contending with conflicting and sometimes mutually exclusive policy choices, necessitating difficult tradeoffs among internal goals as well as between domestic and international goals. The chapters demonstrate that there is very little policy coherence across the global regulatory areas, and that policymakers do not always seem to acknowledge the inconsistency of their own approaches in each area. In some cases, national policy choices respecting cross-border taxation seem to conflict directly with competing national policy goals in cross-border trade and investment. In other cases, national policy choices in one area seem to be constrained by pre-existing international obligations in another. Sometimes, national policy choices seem wholly constrained by historical international ones. When a need for reform in one area arises, multilaterally embedded policy choices in the other areas may interfere if not prevent reform. Each chapter in this Part examines various aspects of these conflicts, with an eye to understanding the legal, social, economic, and distributive aspects of the contemporary tax, trade, and investment landscape.

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)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.445
Threshold uncertainty score0.792

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.002
Science and technology studies0.0020.001
Scholarly communication0.0040.003
Open science0.0010.002
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.4450.281

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 head0.218
Teacher spread0.205 · 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.

Study designNot applicable
Domainnot available
GenreOther

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
Published2024
Admission routes1
Has abstractyes

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