MétaCan
Menu
Back to cohort
Record W3141988429 · doi:10.5206/cie-eci.v46i1.9312

Service-Learning in Egypt: Effects of Demographics, Course Features, and Community Engagement on Civic and Developmental Outcomes for University Students

2017· article· en· W3141988429 on OpenAlexaffvenue
Neivin Shalabi

Bibliographic record

VenueComparative and International Education · 2017
Typearticle
Languageen
FieldSocial Sciences
TopicService-Learning and Community Engagement
Canadian institutionsBrock University
Fundersnot available
KeywordsDemographicsService-learningScholarshipExtant taxonCommunity engagementCivic engagementMedical educationPsychologyGlobeInterpersonal communicationService (business)PedagogyPublic relationsSociologyMedicinePolitical scienceSocial psychology

Abstract

fetched live from OpenAlex

Although service-learning is spreading in various geographic locations across the globe, the majority of extant literature is based in the U.S. Additionally, past research focused largely on investigating student outcomes through this pedagogy with little attention to exploring the impact of variations among service-learning courses and students. This study addressed these gaps by examining how individual differences among students, course characteristics, and overall community engagement may relate to civic and developmental outcomes for college students through service-learning. Sixty one students at a private university in Egypt completed survey questionnaires. Students’ Demographics and Course Characteristics Composites predicted students’ reports of enhanced community awareness. The Overall Community Engagement Composite contributed to students’ reported outcomes of both enhanced community awareness and interpersonal effectiveness skills. The study suggests lines of research for scholars committed to advancing rigorous engaged scholarship and discusses implications for practitioners seeking to deepen service-learning outcomes.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.048
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0020.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.108
GPT teacher head0.415
Teacher spread0.307 · 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 designObservational
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
Published2017
Admission routes2
Has abstractyes

Explore more

Same venueComparative and International EducationSame topicService-Learning and Community EngagementFrench-language works237,207