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Record W2232997870 · doi:10.5539/jel.v5n1p104

Instilling Service Learning to Undergraduate Business Students: A Case Study Approach to Understanding Business-Related Concepts with the Use of Kiva

2016· article· en· W2232997870 on OpenAlexvenueno aff
Sheri Grotrian-Ryan, Kyle Ryan, Alan A. Jackson

Bibliographic record

VenueJournal of Education and Learning · 2016
Typearticle
Languageen
FieldSocial Sciences
TopicService-Learning and Community Engagement
Canadian institutionsnot available
Fundersnot available
KeywordsService-learningInstitutionService (business)Higher educationBusiness educationAcademic institutionQualitative researchMicrofinanceSociologyPedagogyManagementMarketingBusinessPolitical scienceSocial science

Abstract

fetched live from OpenAlex

Higher education continues to place an emphasis on service-learning, specifically within business and management sciences (Kenworthy-U’Ren & Peterson, 2005). A local academic business organization at a small institution chose to embark on a service-learning endeavor. The purpose of this study was for business majors, active in Phi Beta Lambda (PBL), and their advisors/professors to further investigate microlending and other business-related concepts through the use of service learning. Service was provided to the campus community, area high school Future Business Leaders of America (FBLA) students, and to the borrowers of their Kiva loans. The research, qualitative in nature, employed case study methodology. Themes emerged in academic, personal, and civic dimensions as a result of analysis of student responses to guided questionnaires.

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)0.005
metaresearch head score (Gemma)0.005
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 score0.009
Threshold uncertainty score0.026

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.005
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0090.005
Scholarly communication0.0050.003
Open science0.0020.006
Research integrity0.0030.004
Insufficient payload (model declined to judge)0.0030.001

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 head0.368
Teacher spread0.244 · 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

Citations3
Published2016
Admission routes1
Has abstractyes

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