Bibliographic record
Abstract
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 machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".