Crimes and Punishment: Understanding of the Criminal Code
Bibliographic record
Abstract
Knowledge about criminal law is expected in our society. There are many important reasons why accurate knowledge should be expected, such as deterring citizens from engaging in illegal conduct and ensuring that people are making sound decisions about supporting or not supporting changes in the criminal justice system. This study surveyed 301 undergraduate students about their knowledge of criminal laws and the associated sentences. Our results indicate that participants were accurate in defining theft and the ages for legal use of substances and in identifying whether specific scenarios describe acts considered sexual offences, but less able to define the blood alcohol level for impaired driving, dangerous driving, sexual interference, or aggravated sexual assault. With regards to sentencing dispositions, participants were not consistently accurate. They also tended to inflate the likelihood of reoffending in general, particularly violent and sexual offending. Prior exposure to the criminal justice system did not seem to be associated with crime and sentencing knowledge or recidivism estimations. Our findings identify areas where young adults are unaware of legal definitions of crimes and their punishments and point out the need to find innovative ways to educate young adults on the Criminal Code.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.009 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".