Resisting the Hidden Curriculum: Teaching for Social Justice
Bibliographic record
Abstract
This article describes and discusses a collaborative teaching experiment in criminal law undertaken by three teaching colleagues in the first year program at the University of Ottawa. Responding to a sense of collective dissatisfaction with ongoing attempts to integrate critical perspectives into traditionally structured criminal law courses, and wanting to try something different from within a safe collaborative space, we decided to design and deliver a criminal law course with overtly progressive goals. The course was deliberately targeted at creative students who see the law as a vehicle for social justice, was purposely constructed to actively support students in retaining their incoming commitment to social justice lawyering, and was intentionally non-traditional in its content, structure, delivery, and evaluation. The article offers our perspectives on the experiment. We situate the course within the larger context of legal education, the first-year curriculum, criminal law teaching and scholarship, and professional socialization. The content and delivery of the course was linked to the three overarching objectives of first-year courses: 1) the introduction of a substantive subject area; 2) the introduction of legal reasoning, the nature of law and the legal system; and 3) the exploration of professional identity and purpose. The article examines each of these objectives from both a theoretical and practical perspective and connects each objective with course delivery choices and strategies. It concludes with personal reflections on the strengths and weaknesses of this ongoing teaching initiative.
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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 | 0.005 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.011 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".