Bibliographic record
Abstract
Professor Coughlan maintains that the maxim de minimis non curat lex—the law does not concern itself with trifles—ought not be recognized as a criminal defence. He contends that the defence is redundant in light of existing principles of statutory interpretation, alternative defences to challenge improper decisions to bring charges, and the availability of an absolute discharge at sentencing. He further suggests that utilizing the de minimis defence is no different than allowing a constitutional exemption which has explicitly been prohibited by the Supreme Court of Canada. In response, I maintain that Coughlan improperly conceptualizes the de minimis defence as a challenge to prosecutorial discretion. In my view, the defence serves to prevent judges from finding an accused guilty where the consequences would be grossly disproportionate to the harm caused by the offence. Such proceedings should be stayed because the grossly disproportionate effects arise by virtue of instituting criminal process, not imposing punishment. Although the de minimis defence and constitutional exemptions both exempt accused from statutes, the latter are problematic because they conflict with statutory intent. The same cannot be said of defences as the legislature passes offences with knowledge that they will be circumscribed by defences.
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 | 0.027 | 0.085 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.008 | 0.034 |
| Scholarly communication | 0.009 | 0.026 |
| Open science | 0.008 | 0.006 |
| Research integrity | 0.056 | 0.118 |
| Insufficient payload (model declined to judge) | 0.006 | 0.003 |
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".