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Record W1531537592 · doi:10.22329/celt.v2i0.3228

34. Community-Based Learning: Practices, Challenges, and Reflections

2009· article· en· W1531537592 on OpenAlexaffvenueabout
Mavis Morton

Bibliographic record

VenueCollected Essays on Learning and Teaching · 2009
Typearticle
Languageen
FieldSocial Sciences
TopicService-Learning and Community Engagement
Canadian institutionsYork University
Fundersnot available
KeywordsScholarshipCitizenshipSociologyLearning communityEngaged scholarshipCriminal justiceWork (physics)Community engagementPedagogyPublic relationsScholarship of Teaching and LearningPsychologyTeaching methodPolitical scienceTeaching and learning center

Abstract

fetched live from OpenAlex

This paper will highlight an innovate practice in teaching and learning by reflecting on two fourth-year sociology seminar classes that participated in a community-based learning project at York University. Fifty students collaborated in three to six person teams to work on a problem/issue identified by one of five not-for-profit organizations who work with and/or for women as victims, offenders, and/or professionals in the Canadian criminal justice system. Reflections on the process and outcome of the experience offer insights into organizing and engaging in a community-based learning experience as well as point to some of the substantive benefits. These include the opportunity for increased student engagement, access to, and awareness of, course and community related issues, and citizenship. The paper also identifies potential opportunities to incorporate the dimensions of participation and collaboration between institutions of higher learning and the community/world to mobilize knowledge and offer unique scholarship opportunities for faculty.

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 imitation

Not 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.

metaresearch head score (Codex)0.016
metaresearch head score (Gemma)0.018
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.027
Threshold uncertainty score0.084

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0160.018
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0190.022
Scholarly communication0.0150.008
Open science0.0030.010
Research integrity0.0050.006
Insufficient payload (model declined to judge)0.0040.001

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.

Opus teacher head0.108
GPT teacher head0.384
Teacher spread0.276 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
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

Citations6
Published2009
Admission routes3
Has abstractyes

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