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Record W2626300085 · doi:10.15402/esj.v2i2.173

The Frontiers of Service-Learning at Canadian Universities

2017· article· en· W2626300085 on OpenAlexaffvenueabout
Vladimir Kricsfalusy, Aleksandra Zečević, Sunaina Assanand, Ann Bigelow, Marla Gaudet

Bibliographic record

VenueEngaged Scholar Journal Community-Engaged Research Teaching and Learning · 2017
Typearticle
Languageen
FieldSocial Sciences
TopicService-Learning and Community Engagement
Canadian institutionsUniversity of British ColumbiaWestern UniversityUniversity of Saskatchewan
Fundersnot available
KeywordsService-learningCourseworkExperiential learningGraduation (instrument)CurriculumExcellenceSustainabilitySociologyPedagogyMedical educationPsychologyPublic relationsPolitical scienceMedicineEngineering

Abstract

fetched live from OpenAlex

Service learning is a form of experiential learning that cultivates academic development, personal growth, and civic engagement. Students contribute to and learn from community. Service learning empowers students, enabling them to recognize their ability to act as agents of social change. Service learning is gaining momentum as a movement, given its ability to prepare students for the “real world” after graduation. The authors of this article come from health sciences, psychology, and environment and sustainability. Here, we illustrate service learning through four case studies: 1) An innovative team-based service-learning course partnering with older adults, healthcare providers and community agencies (Gerontology in Practice, Western University); 2) A unique curriculum design that includes service learning and interdisciplinary graduate problem-based training and research focused on experimental education (Environmental Sustainability, University of Saskatchewan); 3) An international service learning course that combines intensive coursework and a 3-month placement with a non-profit, community-based organization in Africa (Psychology and Developing Societies, University of British Columbia); and 4) An extraordinary example of an institutional-level commitment to service learning involving 50 courses, 40 faculty, 100 community agencies, and 900 students per year (St. Francis Xavier University). Our goal is to inspire other educators to engage in the pursuit of excellence in higher education through service learning.

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)0.805
metaresearch head score (Gemma)0.428
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Science and technology studies, Scholarly communication, Research integrity
Consensus categoriesMetaresearch
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.902
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.8050.428
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.9030.001
Scholarly communication0.0030.001
Open science0.0030.001
Research integrity0.0000.607
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 head0.123
GPT teacher head0.385
Teacher spread0.262 · 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; both teacher heads agree on what is shown here.

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

Citations1
Published2017
Admission routes3
Has abstractyes

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