Democratising Case Law while Teaching Students
Bibliographic record
Abstract
This article draws on qualitative student feedback and lecturer experience to provide a guide for educators who are interested in creating Wikipedia article based assignments. Using legal cases as an example, this article details how these assignments can encourage students to deepen their understanding of a topic and consider how knowledge can be communicated effectively. In particular, this article focuses on how educators outside of the United States and Canada can navigate Wikipedia’s bureaucracy and how they and their students can contribute information of relevance to smaller jurisdictions on a publicly-accessible repository. This article begins by addressing concerns that educators may have with student use of Wikipedia, while highlighting pedagogical benefits for students who write Wikipedia articles. It goes on to provide a guide for educators who want to create a Wikipedia article writing assignment – in particular, the preparatory steps required to make the assignment effective, how to support students in their writing journey, and how to better ensure that student-authored articles remain available on Wikipedia once uploaded. This article concludes by encouraging educators to consider using Wikipedia as an educational tool, and to teach their students how they can use Wikipedia article writing to contribute to public knowledge.
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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 | 0.014 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.012 | 0.009 |
| Scholarly communication | 0.012 | 0.010 |
| Open science | 0.003 | 0.012 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.012 | 0.003 |
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".