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Record W2150873884 · doi:10.21273/horttech.21.1.119

Meeting Learning Objectives through Service-Learning: A Pomology Case Study

2011· article· en· W2150873884 on OpenAlexaboutno aff
Lauren C. Garner

Bibliographic record

VenueHortTechnology · 2011
Typearticle
Languageen
FieldSocial Sciences
TopicService-Learning and Community Engagement
Canadian institutionsnot available
Fundersnot available
KeywordsService-learningMedical educationCommunity serviceService (business)Quarter (Canadian coin)PsychologyGraftingMedicinePedagogyGeographyPolitical sciencePublic relationsBusiness

Abstract

fetched live from OpenAlex

Undergraduate students enrolled in the introductory pomology course at California Polytechnic State University, San Luis Obispo, CA, from 2007 to 2010, participated in a service-learning project. Students helped the community organization, the California Rare Fruit Growers (CRFG), teach grafting skills to San Louis Obispo County high school students and community members. At the end of each quarter, pomology students completed evaluations of their experience. Results of these evaluations were used to improve teaching methodology and the experience in which the students participated. Self-reported and instructor evaluations of the service-learning project demonstrated that students increased their grafting knowledge and skills, their confidence in learning new skills, and their interest in fruit science and community involvement. The service-learning project enabled students to meet course learning objectives of understanding and becoming experienced in horticultural techniques, such as grafting, and to meet university learning objectives of developing critical thinking and communication skills and increasing community involvement.

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)0.006
metaresearch head score (Gemma)0.010
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 score0.011
Threshold uncertainty score0.030

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.010
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.001
Science and technology studies0.0110.003
Scholarly communication0.0040.003
Open science0.0030.005
Research integrity0.0040.004
Insufficient payload (model declined to judge)0.0050.001

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 head0.085
GPT teacher head0.332
Teacher spread0.247 · 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

Citations1
Published2011
Admission routes1
Has abstractyes

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