ENHANCING THE LEGAL PROFESSION’S CAPACITY FOR INNOVATION: THE PROMISE OF REFLECTIVE PRACTICE AND ACTION RESEARCH FOR INCREASING ACCESS TO JUSTICE
Bibliographic record
Abstract
Recent national reports have documented growing justice gaps in Canada and have identified a compelling need for innovation in the justice sector to better meet the needs of the public. Nurturing a greater capacity for individual, collective, and critical reflection will ensure the legal profession is much better equipped to respond creatively and strategically to a lack of equal access to justice. In this article, I explore the generative and transformative potential of reflective practice – an important professional competency in other professional disciplines, but under-theorized in law, and action research – a dynamic and flexible form of qualitative research for supporting a culture of innovation in the legal profession and the justice system. Reflective capacity is a crucial enabler of innovative thinking, and it undergirds approaches to encouraging individual and systems change emerging from the organizational learning and innovation literature. An enhanced capacity for reflection will also support more generative and “future-forming” dialogues within the profession and between justice system stakeholders. Furthermore, systematically reflecting on disorienting empirical data about the troubling state of access to justice could develop an “access to justice consciousness” in law students and legal professionals, leading to a stronger willingness to take action to narrow the justice gaps. Introducing action research as an unpretentious and effective enabler of profound transformation and innovation in individual and organizational practices offers significant promise for tackling the “wicked problem” of access to justice. Practical illustrations of action research as an enabler of innovation drawn from legal practice are provided.
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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 | 0.146 | 0.136 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.013 | 0.100 |
| Scholarly communication | 0.032 | 0.045 |
| Open science | 0.005 | 0.028 |
| Research integrity | 0.014 | 0.016 |
| Insufficient payload (model declined to judge) | 0.006 | 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".