Bibliographic record
Abstract
This Series is the result of a rather unique endeavour of HiiL: to research the law of the future.Driven by research results that were coming from HiiL and from the small but growing engagements of others (Anne Marie Slaughter's New World Order stands out as an early pioneering work), we had a clear sense that the global legal environment was changing, and that legal and justice actors were both shaping those changes and were constantly working to adapt to them.To understand law's futures, we started a process in which we asked 52 thought leaders in different legal areas to reflect on law in their field two decades away.Their think pieces became The Law of the Future and the Future of Law, volume one.One thing led to another, and with the tremendous support of our hugely innovative publisher, the Law of the Future Series was set up.The Series is meant as a focal point for publications that reflect on the challenges connected with law's futures and what we can do to deal with them.It is perhaps a forum for a new discipline, legal futurology, although the Series is not meant for Jules Verne or George Lucas fantasies (however much we admire and enjoy them).It is a place for serious and thorough thinking about the future place in our lives of a commodity the future badly needs: good rule systems that support stability, prosperity and human dignity.
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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 | 0.021 | 0.029 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.020 | 0.068 |
| Scholarly communication | 0.033 | 0.041 |
| Open science | 0.003 | 0.017 |
| Research integrity | 0.010 | 0.018 |
| Insufficient payload (model declined to judge) | 0.025 | 0.004 |
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".