International Survey about Perceptions of Courts’ Role in Addressing Social Issues through Problem-Solving Courts
Bibliographic record
Abstract
Purpose of the study: Community sentiment is a collective group of attitudes toward an object–such as problem-solving courts–which could differ between populations. This study addresses community sentiment regarding whether the courts should address social problems and whether community sentiment differs based on the type of problem-solving court. It also investigates whether the perceived responsibility of courts to address social issues differs based on country (United States, Australia, and Canada) or individual differences, as well as whether group or individual differences are more predictive of support for problem-solving courts. Method: We surveyed citizens in the U.S., Australia, and Canada using an online survey. Results: We found that participants had positive community sentiment toward all four (drug, homelessness, mental health, tribal wellness/Aboriginal) problem-solving courts and community sentiment did not differ between countries. We also found that endorsement of therapeutic jurisprudence was the largest predictor for community sentiment toward all four courts. Conclusion: We found that sentiment was positive but similar in all countries. Individual differences (e.g., authoritarianism, support for justice principles, and attributions for crime) were stronger predictors than country of residence. This study can help encourage the creation of specialty courts to address social issues.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".