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Record W4252389196 · doi:10.3138/utlj.60.2.623

TREBILCOCK ON TAX AVOIDANCE

2010· article· en· W4252389196 on OpenAlexaffvenue
Benjamin Alarie

Bibliographic record

VenueUniversity of Toronto Law Journal · 2010
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicTaxation and Legal Issues
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsScholarshipTax lawTax avoidanceIncome taxLawWork (physics)Political scienceSociologyLaw and economicsTax reformEngineering

Abstract

fetched live from OpenAlex

Michael J. Trebilcock is by all accounts one of his generation's most prolific and important scholars of law and economics. Through more than 200 articles, book chapters, books, edited volumes, and other academic publications, Trebilcock has made lasting contributions to many fields, including contracts, torts, consumer protection, antitrust, international trade, immigration, regulation, and law and development. In recognition of his teaching and research, he has received awards and distinctions from students, universities, governments, and scholarly societies. The symposium for which this essay was prepared is only the latest token of appreciation for Trebilcock's profound and prominent contributions to the intellectual depth and breadth of legal thought. And yet, despite the accolades, the attention, and the richly deserved scholarly fame, there is a comparatively unlit corner of Trebilcock's oeuvre: the part dealing with income-tax law. Most readers of Trebilcock's more discussed work will not be aware that his scholarly career began, inauspiciously as it might seem, nearly five decades ago with a 224-page llm thesis at the University of Adelaide. Almost unbelievably, this substantial piece of work was dedicated to analysing just a single provision of Australian income-tax law: a general anti-avoidance rule aimed at combating tax avoidance. This essay seizes control of the spotlight that has been trained on Trebilcock's other work and redirects it to his early tax scholarship.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.952
Threshold uncertainty score0.995

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)0.0060.000

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 head0.007
GPT teacher head0.188
Teacher spread0.181 · 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.

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

Citations1
Published2010
Admission routes2
Has abstractyes

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