AbdiAidid and BenjaminAlarie, The Legal Singularity: How Artificial Intelligence Can Make Law Radically Better, Toronto, University of Toronto Press, 2023, 218 pp, hb £31.00
Bibliographic record
Abstract
Reviews on which some authors in this book have embarked in other work.Pieces by practising lawyers may accompany those by forensic musicologists to provide an even thicker picture of the coalface of music litigation.More balance can be achieved with the inclusion of viewpoints that argue for the legal status quo, which may highlight considerations of legal practicability and assist the future development of the field.Finally, the history of music copyright provides another angle ripe for exploration.This book shows that the Western scorecentric compositional practices that had led to the genesis of music copyright in the eighteenth century represent an anomaly amidst how almost all other music has ever been created.For much music, no score exists until litigation.Copyright doctrine must evolve to stay relevant, and for anyone engaging with this research, this book is both field-defining and, arguably, the most invaluable resource to date.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.028 | 0.011 |
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".