The Civil and Criminal Applications of the Identification Doctrine: Arguments for Harmonization
Bibliographic record
Abstract
The identification doctrine refers to the attribution of mental states to a corporation. This article analyzes the two types of situations in which this doctrine is used. Tlte first is where the Crown wishes to hold a corporation liable for crimes requiring proof of mental fault. Canadian Dredge & Dock Co. Ltd. v. R. serves as the cornerstone case in which the doctrine is firmly established as a "sword" against corporations in the criminal law. The second situation in which the doctrine has been used is where the corporation sues in tort, and the defendant wishes to say that the mental state of the plaintiff corporation disentitles recovery. In other words, the doctrine can be used as a "shield" for individuals against claims in tort by corporations.Legislative amendments to the Criminal Code have altered criteria for the doctrine in the criminal law. These changes do not affect the "shield" use of the doctrine, which is still governed by the common law.The author argues for the harmonization of the common law doctrine with its criminal legislative counterpart.
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 | 0.024 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.008 | 0.096 |
| Scholarly communication | 0.009 | 0.013 |
| Open science | 0.004 | 0.010 |
| Research integrity | 0.014 | 0.012 |
| Insufficient payload (model declined to judge) | 0.003 | 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".