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Record W1530683413 · doi:10.22329/celt.v5i0.3438

28. Community-Based Research: Learning About Attitudes Towards the Criminal Justice System

2012· article· en· W1530683413 on OpenAlexaffvenue
Tammy A. Marche, Jennifer L. Briere

Bibliographic record

VenueCollected Essays on Learning and Teaching · 2012
Typearticle
Languageen
FieldSocial Sciences
TopicService-Learning and Community Engagement
Canadian institutionsUniversity of Saskatchewan
Fundersnot available
KeywordsCriminal justicePsychologyService-learningEconomic JusticeValue (mathematics)PedagogySocial psychologyCriminologySociologyLawPolitical science

Abstract

fetched live from OpenAlex

Research points to the pedagogical value of an engaged and community service-learning approach to developing understanding of course content (Astin, Vogelgesang, Ikeda, & Yee, 2000). To help students achieve a better understanding of how the discipline of psychology contributes to the discipline of law, some students in a second year psychology class participated in a community-based research project, partnering with the Elizabeth Fry Society and the John Howard Society. The objective of the study was to determine whether there are differences in attitudes towards the criminal justice system between individuals who have, and have not, been in conflict with the law. The student-researchers interviewed men and women from the John Howard and Elizabeth Fry Societies, who had been in conflict with the law, regarding their attitudes toward the criminal justice system, and compared their responses to those given by undergraduate psychology students who did not participate as student-researchers in the project. Responses revealed some commonalities (e.g., recommendations to change sentencing practices) as well as differences (e.g., satisfaction with the justice system). The students wrote a research report describing the findings of the study as well as their reflections on their experience. In addition to the positive feedback received from the community organizations, the students participating in the project reported that they found it to be a positive, enriching, and rewarding experience.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.028
metaresearch head score (Gemma)0.005
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies, Research integrity
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.335
Threshold uncertainty score0.995

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0280.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0350.000
Scholarly communication0.0010.000
Open science0.0010.000
Research integrity0.0000.010
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.224
GPT teacher head0.408
Teacher spread0.184 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designQualitative
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2012
Admission routes2
Has abstractyes

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