Bibliographic record
Abstract
The year 2014 marks the seventieth anniversary of Saskatchewan's Contributory Negligence Act, which, in its present form, provides for the apportionment of liability according to fault in a tort action and the rights of those at fault to seek contribution inter se for amounts paid to a successful plaintiff.In human life, 70 is a respectable age to celebrate, reflect on, and recount one’s life — perhaps not even too late an age to reform oneself. As in life, so with statutes. What are the Act’s vital statistics? What are the factors that gave rise to its birth? Who are the people who have interacted with it? How has it fared through childhood, youth, adulthood, and middle age and what are its prospects for its more mature years? In light of changes recommended over many decades by the Uniform Law Conference of Canada and law reform bodies in Alberta, Ontario, British Columbia, Saskatchewan, Nova Scotia, and Manitoba, is it time to entertain change?By asking and attempting to answer these questions, we will be heeding a call by UK scholars Arvind and Steele to redress the dearth of scholarly attention paid to legislation in tort law — to take up their challenge of seeing statutes not just as “operating at the margins of the common law…[but] as contributing to the pattern of principles to be found in the law.”
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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.004 |
| Scholarly communication | 0.005 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.006 | 0.007 |
| Insufficient payload (model declined to judge) | 0.014 | 0.010 |
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".