MétaCan
Menu
Back to cohort
Record W2939122253

What learning in the community means for Kenyan university students: A hermeneutic inquiry

2019· article· en· W2939122253 on OpenAlexaff
Charlene VanLeeuwen

Bibliographic record

Venue2019 Conference of the Canadian Society for the Study of Education · 2019
Typearticle
Languageen
FieldSocial Sciences
TopicAdult and Continuing Education Topics
Canadian institutionsUniversity of Prince Edward Island
Fundersnot available
KeywordsTransformative learningKenyaPraxisPedagogySociologyService-learningContext (archaeology)CurriculumPsychologyPolitical science
DOInot available

Abstract

fetched live from OpenAlex

Abstract This paper reports on a hermeneutic study exploring the meaning of community-based learning (CBL) to Kenyan university students. Through in-depth research conversations six students shared their experiences of CBL with various types of human service organizations throughout the country. Their experiences highlight the continuing power of cultural and historical context on students’ learning experiences. Findings delve into distinctive aspects of CBL unique to the Kenyan context related to diversity in Kenya, HIV/AIDs in CBL curriculum and critical CBL reflection. These distinctive issues highlight the complexity of CBL in Kenya. Recognizing critical civic engagement in Kenya is closely associated with the country’s social and cultural contexts, this presentation suggests the pedagogy of discomfort as a critical pedagogy to engage in transformative CBL to mitigate negative reactions to students’ difficult learning experiences in the process of making connections between classroom learning and praxis in Kenyan communities. Additional strategies to guide and support students in critical reflection on their CBL experiences are also discussed.

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)0.018
metaresearch head score (Gemma)0.013
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.031
Threshold uncertainty score0.096

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0180.013
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0030.002
Science and technology studies0.0310.033
Scholarly communication0.0140.007
Open science0.0020.008
Research integrity0.0030.005
Insufficient payload (model declined to judge)0.0030.000

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 head0.052
GPT teacher head0.335
Teacher spread0.283 · 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 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
Published2019
Admission routes1
Has abstractyes

Explore more

Same venue2019 Conference of the Canadian Society for the Study of EducationSame topicAdult and Continuing Education TopicsFrench-language works237,207