Proving Deprivation in Criminal Fraud: Has the Court in R. c. Landry Cast the Net Too Wide?
Bibliographic record
Abstract
There can be no fraud without deprivation. This trite limiting principle is a universally-accepted bedrock of the offence of criminal fraud under s. 380(1) of the Criminal Code. The Quebec Court of Appeal’s recent split decision in R. c. Landry, suggests that even a remote and tenuous risk of financial harm can suffice. But perhaps more troubling is the point at which the majority holds that a risk of harm materializes for the purpose of assessing deprivation at the actus reus stage. According to the majority, it is not once the fraudulent scheme is completed, but rather at the exact moment of the dishonest act. In this piece, I argue that the majority of the Court of Appeal’s approach is incorrect and I instead suggest that the dissenting judgment of Madam Justice Cotnam is more consistent with binding jurisprudence, and that the majority’s decision will have sweeping implications. Most importantly of which will be to further broaden the concept of deprivation to include an even wider scope of conduct and risk of pecuniary harm. I conclude by suggesting that the majority’s analysis to assessing deprivation at the actus reus stage will largely leave little room for the offence of attempt fraud.
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 | 0.015 | 0.037 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.014 | 0.027 |
| Scholarly communication | 0.012 | 0.009 |
| Open science | 0.004 | 0.008 |
| Research integrity | 0.019 | 0.020 |
| 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".