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Record W4402918494 · doi:10.26443/mje/rsem.v58i2.10048

Middle school teachers’ perspectives of how service learning projects contribute to student well-being

2024· article· en· W4402918494 on OpenAlexaffvenueabout
Jennifer Watt, Heather Krepski, Rebeca Heringer

Bibliographic record

VenueMcGill Journal of Education / Revue des sciences de l éducation de McGill · 2024
Typearticle
Languageen
FieldSocial Sciences
TopicService-Learning and Community Engagement
Canadian institutionsUniversity of WinnipegUniversity of Manitoba
Fundersnot available
KeywordsService-learningMathematics educationService (business)PedagogyPsychologySociologyMedical educationBusinessMedicineMarketing

Abstract

fetched live from OpenAlex

The purpose of this study was to explore how teacher practitioners in a Canadian middle school perceive students’ experiences of well-being in student-led service learning projects (SLPs). Through semistructured interviews, we explored five school practitioners’ accounts of how SLPs contributed to student relating and functioning in a well-being context. The themes identified demonstrate how well-being can be deliberately integrated within curricular aspects of schooling, and how student well-being can be enhanced as well as enriched when practitioners include well-being as an aim. We conclude that although students may encounter discomfort in the planning and implementation of SLPs, they provide authentic opportunities to develop student voice and autonomy, which can make education more meaningful to them.

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)0.007
metaresearch head score (Gemma)0.012
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 score0.052
Threshold uncertainty score0.103

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0070.012
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0120.012
Scholarly communication0.0070.003
Open science0.0010.007
Research integrity0.0020.005
Insufficient payload (model declined to judge)0.0030.000

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 head0.261
GPT teacher head0.433
Teacher spread0.173 · 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

Citations0
Published2024
Admission routes3
Has abstractyes

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