Instilling Service Learning to Undergraduate Business Students: A Case Study Approach to Understanding Business-Related Concepts with the Use of Kiva
Bibliographic record
Abstract
<p>Higher education continues to place an emphasis on service-learning, specifically within business and management sciences (Kenworthy-U’Ren &amp; 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.</p>
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
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".