Bibliographic record
Abstract
Machines refine and improve products. Artificially intelligent machines will soon have the same effect on the law. Future developments in artificial intelligence and machine learning will dramatically reduce the costs currently associated with rules and standards. Extending this insight, we predict a world of precisely tailored laws (‘micro-directives’) that specify exactly what is permissible in every unique situation. These micro-directives will be largely automated. If the state of the world changes, or if the objective of the law is changed, the law will instantly update. The law will become ‘self-driving.’ The evolutionary path towards self-driving laws will be piecemeal and incremental. At first, machine-driven algorithms will merely be used to guide humans, but, over time, law will increasingly reflect principles and prescriptions developed by machines. We explore three extensions. First, we examine the possibility that the technology is not merely used to provide information about the state of the law but is also used as means of command by the state. Second, we ask how these technological changes will affect contracting behaviour. Third, we examine the effect of micro-directives on social norms.
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 | 0.010 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.022 |
| Scholarly communication | 0.006 | 0.008 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.009 | 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".