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Record W2914208242 · doi:10.5430/bmr.v8n1p22

Service-Learning as a Catalyst for Community Change: An Empirical Examination Measuring the Benefits of a Life Skills Curriculum in Local At-Risk High Schools

2019· article· en· W2914208242 on OpenAlexvenueno aff
Roxanne Helm-Stevens, Mark Dickerson, Randy Fall

Bibliographic record

VenueBusiness and Management Research · 2019
Typearticle
Languageen
FieldSocial Sciences
TopicService-Learning and Community Engagement
Canadian institutionsnot available
Fundersnot available
KeywordsCurriculumService-learningEtiquetteLife skillsMedical educationService (business)PsychologyWork (physics)PedagogyMathematics educationBusinessMarketingMedicineEngineeringPolitical science

Abstract

fetched live from OpenAlex

This research attempts to measure the impact of service-learning on community recipients – at-risk high school students in urban Southern California. The service-learning project, an integrative, six-week assignment, involves upper-division business majors delivering the Options: Business Education and Life Skills curriculum to at-risk students in two local alternative education high schools. In addition to delivering business education and life skills, a critical design component of the curriculum is the opportunity for college students to be role models and provide mentoring guidance to at-risk high school students. This study used surveys to gather data on student perceptions of four constructs: (1) strengths and values, (2) school and work-related skills, (3) business etiquette and resume building, and (4) future life and career planning. Pre-tests and post-tests were administered to gauge differences in perception during the six-week service-learning project. Results indicated positive effects of the service-learning curriculum overall. Further, the data revealed statistically significant results with particularly noteworthy outcomes in the planning for the future and preparing for the world of work responses.

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)0.013
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 score0.372
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0130.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.002
Science and technology studies0.0020.000
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0000.001
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 head0.110
GPT teacher head0.376
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.

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

Citations2
Published2019
Admission routes1
Has abstractyes

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