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Record W1525080545 · doi:10.22329/celt.v4i0.3276

12. Engaging Students in the Community as Part of Classroom Learning and for Future Benefits

2011· article· en· W1525080545 on OpenAlexafffundvenue
Angela Thompson

Bibliographic record

VenueCollected Essays on Learning and Teaching · 2011
Typearticle
Languageen
FieldSocial Sciences
TopicService-Learning and Community Engagement
Canadian institutionsSt. Francis Xavier University
FundersSt. Francis Xavier University
KeywordsService-learningPsychologyPedagogyMedical educationPerceptionService (business)Medicine

Abstract

fetched live from OpenAlex

This paper shares the survey results of students’ perceptions of the benefits of their experiences with service learning. The service learning experiences were related to children and their physical growth and development and/or to children and their health education. An open-ended questionnaire was sent to 376 graduated students (representing 11 courses over six years) to gather their thoughts regarding the benefits of their service learning experiences several years after completion. Responses were overwhelmingly positive with the most prevalent themes relevant to educational and career goals: “confirmation of future goals,” “practical skill development,” and “enhance my resume and/or application to a particular field of study/occupation.” Given these findings, service learning should be included in post-secondary education not only for the practical experiences that link theory with reality, but also for the potential benefits to students’ future career and education.

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)0.003
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: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.016
Threshold uncertainty score0.052

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0030.001
Scholarly communication0.0040.003
Open science0.0010.005
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0160.003

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 head0.059
GPT teacher head0.333
Teacher spread0.274 · 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 designNot applicable
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

Citations0
Published2011
Admission routes3
Has abstractyes

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