Ahead by a Century: Tim Edgar, Machine-Learning, and the Future of Anti-Avoidance
Bibliographic record
Abstract
Tim Edgar's contributions to our understanding of tax avoidance and anti-avoidance remain ahead of their time. In this paper, the author argues that Edgar's work on building better general anti-avoidance rules (GAARs) was particularly prescient—correct in its claim that tax avoidance can and should be eliminated through effective anti-avoidance measures. The author maintains that although Edgar's position and vision will eventually be realized, Edgar himself did not anticipate the manner in which this would occur. The author's first claim is that the law is incomplete, and this incompleteness problematizes any insistence on the immediate adoption of strict anti-avoidance measures. The author explains how and why the current stage of legal development falls significantly short of completely specifying the law, including the tax law. The author's second claim is that the next decades will bring considerably more sophisticated and effective approaches to legal development. Described, in broad terms, are some of the mechanisms through which our tax systems are moving toward a legal singularity (a state of the law that is functionally complete and well specified). The author proceeds to outline the implications of his two main claims for the future of GAARs and anti-avoidance—specifically, how the realization of a much more complete system of law will leave effectively no further scope for tax avoidance. Tax law, in the asymptotic realization of Edgar's work and vision, will become well targeted and well equipped to address tax avoidance. Tax avoidance as we know it will cease to exist.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.016 |
| Scholarly communication | 0.007 | 0.014 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.006 | 0.011 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".