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Record W2604900511 · doi:10.1386/ctl.12.1.91_1

Far from apathetic: Canadian youth identify the supports they need to speak about and act on issues

2017· article· en· W2604900511 on OpenAlexaffabout
Lorna R. McLean, Jennifer Bergen, Hoa Truong-White, Jenn Rottmann, Lisa Glithero

Bibliographic record

VenueCitizenship Teaching and Learning · 2017
Typearticle
Languageen
FieldSocial Sciences
TopicYouth Development and Social Support
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsFraming (construction)Experiential learningPolitical scienceAgency (philosophy)DemocracyPublic relationsGovernment (linguistics)CivicsPublic administrationVotingSociologySocial sciencePoliticsLawGeography

Abstract

fetched live from OpenAlex

Abstract According to the government agency responsible for tabulating trends in voting patterns, electoral participation in Canada plunged steadily from the 1990s to early twenty-first century; most of the decline is attributed to a dwindling of interest among youth voters, specifically those between 18 and 24 years of age. Recent national and international research links experiential learning with increased civic engagement. By framing our community student-learning project around student engagement and issues that the students raised, our study evolved as a joint collaboration among a government agency (Elections Canada), a national youth leadership programme (Encounters With Canada) and a Canadian university’s Faculty of Education at the University of Ottawa. This study gives particular voice to students to help educators better understand how teenagers see themselves as citizens, what issues they identify as significant, and what resources and materials they claim they need to engage with in the democratic process.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies, Scholarly communication
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.396
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0100.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.038
GPT teacher head0.328
Teacher spread0.290 · 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 teacher head, not a consensus.

Study designObservational
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

Citations7
Published2017
Admission routes2
Has abstractyes

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