Film as a Complement to the Written Text: Reflections on Using <i>The Sterilization of Leilani Muir</i> to Teach <i>Muir v. Alberta</i>
Bibliographic record
Abstract
In this article the authors look at their experiences teaching the trespass torts to law students using a documentary film about Muir v. Alberta. The case was brought by Leilani Muir against the government of Alberta for battery and false imprisonment and for sterilizing her without her knowledge or consent. The documentary follows Muir’s court case, and interweaves her personal story with the larger social history of the eugenics movement and the development of The Sexual Sterilization Act. The authors begin with a description of the Muir documentary and a discussion of the ways in which the texts, written and filmic, work together in the context of telling Muir’s story. The authors then discuss film as a medium for telling legal stories. Finally, the authors reflect on their classroom experiences with the various Muir texts, and the ways in which the film assists them in teaching both the particular case and torts more generally. The authors suggest that complementing case reports with documentaries about them, or events related to the case, helps to provide alternative and sometimes counter stories to the official account.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.007 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.016 | 0.015 |
| Scholarly communication | 0.011 | 0.005 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.004 | 0.009 |
| Insufficient payload (model declined to judge) | 0.008 | 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".