Reflective Practice & Reflective Inquiry: A Critical Imperative for Enhancing Law Student Learning and Legal Professionalism
Bibliographic record
Abstract
Twenty-first century legal professionals need a flexible and reflective legal education that emphasizes self-assessment and self-efficacy, supports lifelong learning, and builds the capacity for innovative thinking, and responding creatively and constructively to “wicked problems”. This article explores the benefits that “reflective practice”, a core competency in other professions, offers for enhancing the education of legal professionals. Encouraging and modelling reflective practice is best started in law school. To facilitate a dialogue about how reflective practice might be integrated into the law school curriculum, a conceptual framework is outlined. To help envision how reflective practice might be operationalized, examples of reflective methods to help develop a reflective practice competency are provided. Reflective practice and reflective inquiry offer the potential to enhance law student learning and more systematically develop professional expertise, nurture a positive professional identity and a stronger sense of legal professionalism, while supporting students to become both “justice ready” and “practice ready”. Amongst other imperatives for enhancing the education of legal professionals, various national reports call for strategic, collective and aligned action to enhance legal education to better prepare future legal professionals to respond to a growing gaps in access to justice, and predictions of a disruptive and challenging future for the legal profession.
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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 | 0.128 | 0.167 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.010 | 0.085 |
| Scholarly communication | 0.028 | 0.023 |
| Open science | 0.004 | 0.017 |
| Research integrity | 0.012 | 0.020 |
| Insufficient payload (model declined to judge) | 0.003 | 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".