An assessment of students’ attitudes toward wrongful convictions / Bala Usman Chamo
Bibliographic record
Abstract
The study attempts to assess students‟ attitudes toward wrongful convictions. It tried to replicate Ricciardelli, Bell and Clow‟s (2009) Canadian study using an undergraduate student sample from the United Kingdom. This will help to understand whether the students‟ attitudes toward wrongful convictions differ between the two studies. This study adopts a between group design, in which one 150 participants were involved. The attitudes of 81 Criminal justice students and 69 Noncriminal Justice Students were compared. The study also assessed students‟ attitudes according to year of study, in which 74 year one students‟ were compared with 71 year three students. Additionally, attitudes were examined according to gender. The findings revealed that criminal justice students differ from noncriminal justice students in their attitudes toward wrongful convictions. Years three gave higher estimates of the frequency of wrongful convictions and were more supportive for the Blackstone ratio than year one students. Nevertheless, year one students were more supportive of the need to train the criminal justice professionals than year three students. The result showed no difference between the participants‟ confidence in the criminal justice system. No differences were found between males and female in terms of their attitudes toward wrongful convictions. The implications of the findings were discussed. Maximum of 250 words.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".