BUILDING BETTER LAW: HOW DESIGN THINKING CAN HELP US BE BETTER LAWYERS, MEET NEW CHALLENGES, AND CREATE THE FUTURE OF LAW
Bibliographic record
Abstract
The legal profession faces increasing challenges to the relevance, utility, and acceptance of law and the rule of law as tools of social organization that are important and essential to human beings. Often the issues which challenge law and legal systems seem perennial, obstinate, and intractable. In order to remain relevant to the societies it serves, the law needs to innovate. We need to find new ways of thinking about law as a human designed and deliberate system of social organization. In this context, adopting an innovation mindset is an important starting point. “Design thinking” offers us a description and practice of an innovation mindset that can be and is employed in a variety of professional contexts. This article is an introduction to design thinking, its challenges, and its possibilities for law. It postulates that in fact design thinking as a concept and as a set of techniques is particularly well suited for use in law, and that we actually employ many of its techniques already. The article argues that by bringing these techniques into sharper focus, we can both recognize how we are in some ways using them already, and more importantly, how they can be deployed in even more useful and innovative ways to “build better law” at all scales of the legal endeavour, from individual service to legal systems.
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 | 0.025 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.007 | 0.052 |
| Scholarly communication | 0.028 | 0.035 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.008 | 0.010 |
| Insufficient payload (model declined to judge) | 0.008 | 0.003 |
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".