Bibliographic record
Abstract
ince the decision of the Supreme Court of Canada in Whiten v Pilot Insurance Co, the dire predictions of some academic and industry commentators have largely failed to come true. 1 Fears abounded that Whiten's $1,000,000 award of punitive damages 2 would open the floodgates, with Canadian courts becoming swamped in the perceived morass of American tort law. 3 In Manitoba, punitive damage awards since Whiten have become neither larger nor more frequent.4 The anxiety of the commentators, although not fully realised, reflects the precarious position of the law governing punitive damages in Canada.Many of their fears were reflected in the decision of Binnie J (and LeBel J in dissent) in Whiten B.A., LL.B. (Manitoba).Articling student-at-law at the Manitoba Prosecution Service.The views expressed herein are his own and do not necessarily reflect the views or opinions of the Manitoba Prosecution Service. 1 Whiten v Pilot Insurance Co, 2002 SCC 18, [2002] 1 SCR 595 [Whiten SCC]. 2 Following the practice of the Supreme Court of Canada, I have used the term "punitive damages" in this paper instead of "exemplary damages."The terms are synonymous.3 See e.g.Rudy V Buller, "Whiten v Pilot: Controlling Jury Awards of Punitive Damages" (2003) 36 UBC L Rev 357; Roger G Oatley, "Punitive damages in Canada: Whiten v Pilot Insurance, "The Insurer from Hell"" (Winter 2002) 21 Advocates' Soc J No 3 14; Canadian Underwriter, "Court upholds $1 million punitive award against Pilot" (1 March 2002), online: Canadian Underwriter ; Eleni Maroudas & Sivan Tumarkin, "Significant Damage Awards Including Punitive and Economic Loss Awards Across Canada and the Meaning of "Serious Impairment" in Threshold Cases", online: Samfiru Tumarkin LLP .4 See Appendix I for recent punitive damage
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 | 0.004 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.034 | 0.007 |
| Scholarly communication | 0.012 | 0.002 |
| Open science | 0.005 | 0.005 |
| Research integrity | 0.013 | 0.012 |
| Insufficient payload (model declined to judge) | 0.013 | 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".