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Record W2156067140 · doi:10.1177/0743558413502532

Creating Effective Civic Engagement Policy for Adolescents

2013· article· en· W2156067140 on OpenAlexaffabout
Ailsa Henderson, S. Mark Pancer, Steven D. Brown

Bibliographic record

VenueJournal of Adolescent Research · 2013
Typearticle
Languageen
FieldSocial Sciences
TopicService-Learning and Community Engagement
Canadian institutionsWilfrid Laurier University
Fundersnot available
KeywordsCivic engagementGovernment (linguistics)Diversity (politics)PsychologyPublic engagementCommunity engagementService (business)Public relationsPublic servicePublic policyCommunity serviceService-learningPolitical sciencePedagogyPolitics

Abstract

fetched live from OpenAlex

For high school community service programs to have a positive impact on subsequent civic engagement, students must volunteer in a sustained manner and must evaluate their volunteering experiences positively. Using a survey with 1,293 respondents and 100 semistructured interviews with past participants of the mandatory community service program implemented by the Ontario provincial government in 1999, the authors identify how and why students generate positive evaluations of community service requirements and whether the diversity of implementation or the mandatory nature might account for negative reactions to volunteering. The authors discuss the significance of these findings for academic debates about community service and for discussions about the ways in which public policy can promote the civic engagement of young people.

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)0.010
metaresearch head score (Gemma)0.014
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 score0.022
Threshold uncertainty score0.089

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0100.014
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0060.003
Scholarly communication0.0060.003
Open science0.0010.009
Research integrity0.0030.003
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 head0.115
GPT teacher head0.459
Teacher spread0.345 · 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

Citations30
Published2013
Admission routes2
Has abstractyes

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