Bibliographic record
Abstract
In the Harvard Law Review in 1897, Oliver Wendell Holmes, Jr., wrote that ‘[f]or the rational study of the law the black-letter man may be the man of the present, but the man of the future is the man of statistics and the master of economics.’ 1 Now, nearly 120 years later, the future that Holmes foresaw is arriving. Much of the global population has transitioned from an analogue, paper-based world with unreliable, slow, and costly communication to a digitally connected world with almost universal real time and nearly costless communication. And within this digital world, we are witnessing substantially greater availability of data and improved methods of machine learning through advances in computer-assisted modelling and inference. The implications for law of an abundance of data of all kinds and dramatically more effective statistical tools are becoming visible. The ultimate consequences for law will be profound. I propose referring to the culmination of these developments as ‘the legal singularity.’ The legal singularity will affect all areas of the law. For the purposes of illustration, I focus my attention here on tax law. I predict that the coming decades will witness three gradual transitions as the legal singularity draws nearer: (a) improved dispute resolution and access to justice in tax law, primarily through the transition from our current reliance on standards (adjudicated ex post) to greater reliance on query-able systems of complex rules (knowable ex ante); (b) a transition to superior and increasingly more complete specifications of tax law (that is, a gradual transition from the complex, unwieldy, uncoordinated tax systems of today to tax systems that are massively complex and yet precisely and effectively distribute benefits and burdens); and (c) with the realization of the legal singularity, a complete specification of tax law (and, indeed, all of the other areas of law), which will thenceforth remain (more or less) in positive and normative equilibrium. The equilibrium achieved by the legal singularity will be a type of reflective equilibrium along the lines described by John Rawls in A Theory of Justice. 2
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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 | 0.012 | 0.030 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.006 | 0.035 |
| Scholarly communication | 0.009 | 0.024 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.011 | 0.016 |
| Insufficient payload (model declined to judge) | 0.007 | 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".