Write a Wikipedia Article for Law School Credit - Really?
Bibliographic record
Abstract
Most law school assignments are produced and consumed in a dyadic relationship of student-writer and instructor-reader. But consider a different scenario, one in which the fate of the work is presumptive publication to the world; in which feedback from any interested reader is potentially instantaneous; in which the instructor’s role is that of coach or mentor through the writing and publishing process as well as assessor of the work; and in which the student’s work, in turn, contributes to providing worldwide access to free legal information. The world we are talking about is that of writing or editing Wikipedia articles for law school credit. In this Article, we describe that world and the small part we played in it as law professor and law student in editing a Wikipedia article as an optional component of an upper-year Canadian law school course.In Part I, we set out some of the background to Wikipedia. This includes a discussion of its history, philosophy and policies; the relationship between Wikipedia and higher education; and the relationship between Wikipedia and law. In this Part, we make a pedagogical case for turning law students from “consumers” to “producers” of Wikipedia’s legal content. In Part II, we talk about what we did as professor and student in the course, focusing on the editing of a specific Wikipedia “stub” article — that is, an article clearly in need of editing and further development. In Part III, we consider the assessment of student contributions to Wikipedia. This includes a discussion of various rubrics and the Wikipedia Education Project Syllabus, which provides a general template for a twelve-week course emphasizing Wikipedia content. Part IV summarizes our reflections on the exercise, including both its limitations and opportunities. The Appendix provides some links to resources for professors and students who want to experiment with writing or editing Wikipedia articles for law school credit.
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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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.030 | 0.009 |
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".