Bibliographic record
Abstract
Property on Trial is a collection of 14 studies of Canadian property law disputes — some well-known, some more obscure — that have helped to shape the contours of the principles and rules of property law over 150 years. These studies, written by some of Canada's leading legal historians, range in time from a discussion of a nineteenth-century dispute over the ownership of seal pelts in Newfoundland to modern questions of what constitutes private property in a digital age. They investigate the relationship between private and public interests in property; the limits of private property owners' rights in relation to others, particularly neighbours and family; and the intersection of property law principles with other branches of the law, including criminal law, family law, and human rights.\nThe authors describe, in rich detail, the social, cultural, and political contexts in which the events unfolded, the backgrounds and personalities of the litigants, the skills of the lawyers, and the judicial attitudes of the day. On the one hand, Property on Trial is a collection of thoughtful and compelling stories about conflict in a wide variety of contexts, each with its own heroines and heroes, villains and ne'er-do-wells, winners and losers. On the other, it is an insightful look at the history of property law doctrine in Canada.
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 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.016 |
| Science and technology studies | 0.059 | 0.021 |
| Scholarly communication | 0.015 | 0.004 |
| Open science | 0.005 | 0.009 |
| Research integrity | 0.010 | 0.012 |
| Insufficient payload (model declined to judge) | 0.011 | 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".