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Record W2800983152 · doi:10.37333/001c.29769

Indigenous Perspectives on Community Service-Learning in Higher Education: An Examination of the Kenyan Context

2017· article· en· W2800983152 on OpenAlexaff
Charlene VanLeeuwen, Lori E. Weeks, Linyuan Guo-Brennan

Bibliographic record

VenueInternational Journal of Research on Service-Learning and Community Engagement · 2017
Typearticle
Languageen
FieldSocial Sciences
TopicService-Learning and Community Engagement
Canadian institutionsDalhousie UniversityUniversity of Prince Edward Island
Fundersnot available
KeywordsIndigenousExperiential learningKenyaPerspective (graphical)Context (archaeology)Extant taxonService-learningSociologyPedagogyColonialismIndigenous educationService (business)Engineering ethicsPolitical sciencePublic relationsGeographyEngineeringComputer science

Abstract

fetched live from OpenAlex

To understand community service-learning (CSL) in global contexts, an Indigenous perspective is needed to reflect the range of contextual and historical issues. Theoretical discussions of CSL generally reference theories of experiential and reflective learning; however, work in critical pedagogy and anti-colonial discourse can be utilized to generate a framework that embraces the breadth and depth of CSL in different regions. Extant research on CSL in Africa has found that student learning and development are influenced by pressures faced by the higher education system as well as historical and contextual issues encountered by students while engaged in CSL. As discussed in this article, incorporating an Indigenous perspective within existing theoretical frameworks can enable the development of models, pedagogical approaches, and practices that reflect needs of Kenyan communities. The authors present a rationale for further CSL research in Kenya to ensure culturally sensitive, theoretically sound, and non-exploitive CSL that fosters positive outcomes for students, partner organizations, communities, and higher education institutions.

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)0.030
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Science 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 score0.281
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0300.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0100.000
Scholarly communication0.0010.001
Open science0.0040.001
Research integrity0.0000.011
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 head0.244
GPT teacher head0.458
Teacher spread0.214 · 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

Citations8
Published2017
Admission routes1
Has abstractyes

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