Bibliographic record
Abstract
Abstract The Article examines a novel regulatory approach, called the “innovation sandbox,” in the context of the legal profession. The Article makes the claim that the “sandbox” regulatory model is in fact better suited to fostering innovation in the legal services arena than it is in the financial technology, or fintech, arena in which the sandbox concept was developed. However, any effort to transplant a technique from one context to another needs to be carefully considered. This Article is comparative across disciplines—financial regulation and legal services regulation—and across jurisdictions, considering the United Kingdom, the United States, and Canada. The Article analyzes the key normative assumptions underlying the sandbox concept in fintech: that innovation is beneficial almost by definition, that consumer choice and market preferences can be counted on to winnow out “bad” ideas, and that a private sector-driven strategy based on lifting “regulatory burdens” is an effective way of advancing the public interest. These assumptions, which are fairly mainstream in financial regulation, are unfamiliar if not alarming when transposed to legal services regulation. After discussing normative and contextual differences between these regulatory environments, this Article argues that although these ideas may seem problematic at first glance, the sandbox approach may in fact be particularly promising. It may actually be possible to foster legal innovation, advance the public interest, and take meaningful steps to address the access to justice crisis using an innovation sandbox. However, success will come down to how well the sandbox is implemented. The Article’s second half provides a roadmap, informed by rule of law and justice concerns and based on experience from the fintech sector, for how to create a high-functioning, accountable, equity-conscious innovation sandbox for legal services.
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 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.006 | 0.024 |
| Scholarly communication | 0.015 | 0.010 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.010 | 0.011 |
| Insufficient payload (model declined to judge) | 0.010 | 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".