Ignorance of Law, Criminal Culpability and Moral Innocence: Striking A Balance Between Blame and Excuse
Bibliographic record
Abstract
The ignorance of law rule, embodied in the maxim ignorantia juris non excusat, occasionally conflicts with the fundamental tenet of the criminal law that the morally innocent should not be penalised. It is argued that this rule needs to be reformulated so that reasonable ignorance of law is not excluded as a relevant consideration in criminal matters. A comparative approach is adopted and the discussion is primarily based on the laws of Australia and England with some reference to Canadian and United States jurisprudence. The Penal Code's apparent unequivocal rejection of ignorance of law as a defence has the consequence that local courts have had almost no opportunity to consider the ignorance of law rule and possible exceptions thereto, apart from merely reaffirming that mistake of law is not a defence. The comparative analysis suggests that the ignorance of law rule, while still applicable, has been whittled by several exceptions, the broad thrust of which is that a person who is reasonably ignorant of the law is in fact morally innocent and not deserving of criminal punishment.
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 | 0.003 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.031 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
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".