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Record W2334737917

Beyond 40 Hours: Meaningful Community Service and High School Student Volunteerism in Ontario

2011· dissertation· en· W2334737917 on OpenAlexaboutno aff
Hoda Farahmandpour

Bibliographic record

VenueTSpace · 2011
Typedissertation
Languageen
FieldSocial Sciences
TopicService-Learning and Community Engagement
Canadian institutionsnot available
Fundersnot available
KeywordsService (business)Service-learningPsychologyMedical educationMathematics educationPedagogyBusinessMedicineMarketing
DOInot available

Abstract

fetched live from OpenAlex

This study explores whether students in the mandated Ontario high school community service program consider their service requirement to be meaningful; the relationship between meaningful service and subsequent service; and other factors related to a meaningful experience and future service. A secondary analysis was conducted using a survey of 1,341 first-year university students, collected by a research team led by Steven Brown of Wilfrid Laurier University. The main finding is that meaningful service is a predictor for subsequent service and can contribute to individual and social change. Meaningful service opportunities help address a gap in service learning literature, which is the impact of service on communities, perhaps by underestimating the capacity of youth to contribute to social change. Three policy recommendations emerge: curriculum should be created to enable students to serve more effectively; program structure is necessary for reflection; and nonprofit agencies can meet both of the above needs.

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)0.001
metaresearch head score (Gemma)0.004
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.114
Threshold uncertainty score0.230

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0080.003
Scholarly communication0.0030.001
Open science0.0010.003
Research integrity0.0010.001
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 head0.043
GPT teacher head0.342
Teacher spread0.299 · 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

Citations5
Published2011
Admission routes1
Has abstractyes

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