What We Talk About When We Talk About Skills: Toward a Taxonomy of Legal Skills for Teaching and Leaning in Ontario
Bibliographic record
Abstract
When stakeholders in legal education discuss skills teaching and learning, do they share conceptual and linguistic common ground? Or do they simply assume that such common ground exists? This qualitative study explores those questions against the backdrop of a rapidly changing practice and licensing environment in Ontario. The goal of the study was to create a preliminary taxonomy that describes in the broadest possible terms what stakeholders in Ontario legal education collectively identify and refer to as legal skills. Using the method of computer-assisted qualitative content analysis, text data referring to skills was extracted from publicly available documents produced by three stakeholder groups: Ontario law firms and bar associations, collectively representing the Profession; the Law Society of Upper Canada and the Federation of Law Societies of Canada, representing the Regulator in Ontario; and Ontario law schools. Text excerpts were classified and assigned to thematic categories based on both inductive and deductive coding methods. This resulted in a taxonomy of over 100 individually identified skills referenced by the stakeholders. Whereas other studies have described skills in the narrow terms of what lawyers do in practice, in order to drive skills curriculum in law schools, the method used here collected data without prioritizing one voice over another. Thus, the resulting taxonomy, rather than defining skills in accordance with a single stakeholder’s interests, instead expands the way in which legal educators might imagine and program skills teaching and learning. It provides common ground for discussion among legal educators about skills curriculum, and encourages stakeholders to work collaboratively to design curriculum that is responsive to our students’ needs for innovative legal education, which will in turn prepare them to successfully navigate the demands of practice in a “new law” environment.
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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 | 0.006 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.017 | 0.020 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".