A Framework for Data-Driven Legal Regulatory Reform
Bibliographic record
Abstract
To fulfill its responsibility to the Washington Supreme Court to innovate new avenues for persons not currently authorized to practice law to provide legal and law-related services, the Washington Supreme Court’s Practice of Law Board (POLB) studied how to improve the regulation of legal services through legal regulatory reform, with an eye towards closing the access to justice gap. As the traditional processes for legal regulatory reform generally rely on the anecdotal experiences of participants in the reform; take significant time to create, evaluate, and implement; and rarely go on to measure whether the reform had the intended goal without harmful or unintended consequences, the POLB looked into the feasibility of a “regulatory sandbox” to test legal reform proposals and move toward data-driven decisions that measure the impact of the reform.\nThe POLB is aware and fully acknowledges that other jurisdictions in the United States and Canada, including Utah, are implementing sandboxes for legal regulatory reform. Sandboxes have been used for managing reform in other areas, such as financial regulation. But the POLB is also looking for a methodology that permits the POLB and others to assess the potential risks and the benefits of the reform. The POLB set out to develop a methodology that would allow innovators, regulators, access-to-justice advocates, and the public to use a consistent set of processes for designing, maintaining, and participating in a sandbox that would provide adequate guardrails to protect the public and others while reforms are tested and relevant data is collected. The POLB believes that the framework outlined in this paper is such a methodology.\nThe audience for this paper is people who want to enable regulatory reform because this framework provides possible measurements for guiding regulatory decisions, but it is not designed to be so rigid that all of its components must be used. Other components, such as a different algorithm for measuring the impact of a reform on the access-to-justice gap, could be substituted for the method proposed with this framework. Hopefully this framework provides a means to think critically about the impact a reform might have—so there is a better output with minimal unintended consequences.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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; both teacher heads agree on what is shown here.
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".