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Record W3036672386 · doi:10.5206/cie-eci.v48i2.10791

In the Pursuit of Justice: A Case Study on the Role of Service-Learning in Developing Civic Engagement among College Students in Egypt

2020· article· en· W3036672386 on OpenAlexaffvenue
Neivin Shalabi

Bibliographic record

VenueComparative and International Education · 2020
Typearticle
Languageen
FieldSocial Sciences
TopicService-Learning and Community Engagement
Canadian institutionsBrock University
Fundersnot available
KeywordsService-learningCivic engagementExperiential learningPopulationPedagogyCommunity engagementPsychologyCivicsStudent engagementPublic relationsPoliticsSociologyPolitical science

Abstract

fetched live from OpenAlex

Service-learning is a form of experiential education that connects classroom instruction with community service for the benefits of the partners involved. While the effects of service-learning on college students are well documented in Western settings, considerably less is known about these effects in Eastern contexts. Given the current profound political changes in Egypt and the greater Arab world, this research utilized a case study design to explore the potential of service-learning for developing civic awareness among college students at a university in Northern Egypt. Findings revealed that participation in service-learning allowed students valuable opportunities to connect with others from backgrounds different from their own. Through their work in the community, students gained important civic skills, including thinking critically and addressing public problems, developing perspective-taking positions, and enhancing coping capabilities. The study outcomes suggest a cultural shift in Egypt that validates young adults as a productive segment of population thus affording them structured opportunities for civic engagement through which they could exercise their leadership skills and effect positive change in their communities. The ongoing critical engagement of students in their communities where they grapple with questions about the structural causes of inequalities in society is pivotal for service-learning to be a truly empowering learning experience.

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)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.027
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
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 head0.180
GPT teacher head0.446
Teacher spread0.266 · 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.

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

Citations1
Published2020
Admission routes2
Has abstractyes

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