A Property Law Reader: Cases, Questions, and Commentary, 5th ed., Preface and Table of Contents
Bibliographic record
Abstract
Nobody has been more influential over the past generation in the teaching of property law in Canada than Bruce Ziff. His Principles of Property Law is the foundational textbook on the subject. A Property Law Reader: Cases, Questions, and Commentary, which he first published as a sole author in 2004, has become, over three subsequent editions, the most widely used teaching material for property law in the country. Bruce retired from teaching property law in 2019. His retirement left major holes not only at the University of Alberta, where he taught for decades, but also throughout Canada in terms of guiding students, mentoring professors, and developing teaching materials and other resources for property law.\nBruce had brought in Jeremy de Beer, Douglas Harris, and Margaret McCallum to collaborate with him on the 2nd, 3rd, and 4th editions of A Property Law Reader, but with his retirement, and also with Margaret’s, after years at the University of New Brunswick, the 5th edition is the product of a new scholarly collaboration with Tenille Brown and Patricia Farnese joining Jeremy and Doug. Although Bruce has stepped aside entirely from this 5th edition, his intellectual contributions remain profound. Much of the material within chapters has been updated or replaced, but the 5th edition retains the structure and organization that Bruce initially conceived. It also retains many of Margaret’s contributions to the selection of material and the commentary.\nThe preface of the 4th edition began with this statement: “Property law—that body of rules which describes and defines relationships between people with respect to things—involves many choices.” The opening paragraphs continued by emphasizing that these choices, explicit or implicit, involve important decisions about the allocation of resources, and further, that we needed to interrogate the justifications for these decisions. The materials in the 4th edition, and in this 5th edition, return repeatedly to the justifications for particular rules and to ask whether they remain convincing. Indeed, the collection of materials was designed to enable an investigation of property law rules, and of the justifications for them.
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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.087 | 0.038 |
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".