Law & Entrepreneurship in Global Clinical Education
Bibliographic record
Abstract
As clinical legal education (CLE) continues to evolve and prepare practice-ready lawyers, and governments worldwide focus on the multilayered impact of technology, automation and artificial intelligence, there is a pressing need to examine law and entrepreneurship through the lens of global clinical legal education. The range of issues include: corporate social responsibility, disruptive technologies, microbusiness, social entrepreneurship, social impact investing, the creative economy, sustainable local economies, cooperatives and shared work, and inclusive entrepreneurship.\nIndeed, new legal entities like benefit corporations and low profit limited liability companies (L3Cs) have emerged to address contemporary legal needs and in the United States. the notion of an entrepreneurial mindset is prominent. Many of today’s law students are Millennial generation, ages 18-34, while others are digital natives who have not known a world without technology.\nBusiness law clinics (BLCs), also referred to as transactional clinics, representing for profit, nonprofit or nongovernmental (NGOs) organizations and social enterprises aim to support the growth of entrepreneurial ecosystems while promoting social and economic justice. BLCs teach law students substantive law, practical skills and professional values. Indeed, BLCs with a social and economic justice perspective can help law students, the next generation of leaders, to develop critical analytic skills and insights into how entrepreneurship supports and sometimes hurts human rights and civil society efforts.\nPart one of this article examines the evolution of global CLE in western countries like the United States, United Kingdom, Canada, Australia, and in Georgia and Croatia.\nPart two discusses a more recent phenomenon in CLE, the emergence of BLCs, which expand the clinical experience beyond the courtroom to the boardroom, and the differences and similarities between litigation and transactional legal clinics.\nPart three examines the rise in BLCs globally, and contains case studies of the global experience in transactional CLE with perspectives from Georgia, Croatia, Australia, Canada and the U.K.\nPart four considers the unique pedagogical and programmatic aspects of BLCs, such as redefining “practice-ready,” teaching Millennials, and collaboration as a lawyering skill.\nPart five reflects on the significance of BLCs now.\nIn Part six the article concludes by looking to the future of BLCs in a global context.\nThe article also includes an Appendix 1 with BLC Lawyering Competencies and Learning Outcomes and Appendix 2 with a Checklist for Starting or Re-Imagining a BLC.
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 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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.012 | 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; 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".