Changing Attitudes toward the Criminal Justice System: Results of an Experimental Study
Bibliographic record
Abstract
Polls have suggested that fewer than half of Canadians have confidence in the criminal justice system (CJS) as a whole. Low levels of confidence are problematic, as the CJS relies on public support to function effectively. Previous research has found that attitudes toward the CJS are typically based on misperceptions and misinformation, with the public being unaware of the functioning of the CJS as well as of crime trends. Therefore, it seems logical to posit that providing the public with factual information about crime and criminal justice may lead to increased confidence. Past studies have shown that, in general, public education can lead to increased confidence; however, questions pertaining to the mode of delivery have been raised, particularly in regards to how ‘active’ the individual should be in the learning process. The present study was conducted to assess the influence of mode of delivery on CJS knowledge and attitudes. As has been found in past research, participants who received CJS information had a higher level of knowledge than did controls, who received information about Canada's health care system. Interestingly, the type of learning (active vs. passive) did not have an effect on CJS knowledge; however, an effect was observed in regards to confidence and satisfaction: Participants who received CJS information through active learning were more confident in the CJS and had a higher level of satisfaction. These results have important implications for real world interventions.
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 | 0.009 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 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".