Tort Law : Cases and Commentaries
Bibliographic record
Abstract
This casebook provides an introduction to tort law: the law that recognises and responds to civil wrongdoing. The material is arranged in two parts. Part I comprises 1-11 and addresses intentional and dignitary torts and the overarching theories and goals of tort law. Part II comprises 12-25 and addresses no-fault compensation schemes, negligence, nuisance, strict liability, and tort law’s place within our broader legal systems. This casebook is designed to complement the 1L curriculum in common law Canadian law schools. It does not try to present an exhaustive overview of all of Canadian tort law. Instead, it focusses on the central doctrines and topics that are most commonly taught in torts courses. [An updated edition of this casebook was uploaded on 2024-06-07.] [OER Description: This tort law text was developed to support graduate students in Canadian law schools. Each chapter of this open educational resource is largely self-contained to support instructors assigning sections to suit their syllabi. The text includes cases, summaries, and links to formative assessment quizzes based on the casebook content, providing immediate feedback to students. The most recent version of this OER can be found in CANLII.]
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 | 0.005 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.006 | 0.003 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.006 | 0.005 |
| Insufficient payload (model declined to judge) | 0.088 | 0.024 |
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".