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Record W4410829635 · doi:10.1080/14733285.2025.2511788

Democratic citizenship education in a Canadian youth city council: the case of the Commission Jeunesse Gatineau (CJG)

2025· article· en· W4410829635 on OpenAlexafffundabout
Stéphanie Gaudet, Joannie Jean, Mariève Forest

Bibliographic record

VenueChildren s Geographies · 2025
Typearticle
Languageen
FieldSocial Sciences
TopicEducator Training and Historical Pedagogy
Canadian institutionsUniversité du Québec en OutaouaisUniversity of Ottawa
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsCitizenshipCommissionDemocracyPolitical sciencePublic administrationMedia studiesSociologyLawPolitics

Abstract

fetched live from OpenAlex

In North America, an increasing number of youth city councils have been created since the United Nations Convention on the Rights of the Child (UNCRC) was ratified in 1989. These councils facilitate and encourage youth participation within cities. This paper presents the case study of the Commission Jeunesse Gatineau (CJG), the oldest city youth council in the French-speaking province of Québec, whose members are aged 13 to 17. This youth council presents itself as a school of citizenship education. The data show that this initiative recognizes youth’s agency and political citizenship through a diversity of democratic citizenship approaches: (1) liberal, (2) deliberative, (3) participative, and (4) critical. Above all, we demonstrate how youth socialize through contentious politics. The data show that the CJG is an environment for young people to broaden their repertoire of actions and knowledge related to social issues while developing their critical-thinking skills and political actions.

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)0.004
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: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.099
Threshold uncertainty score0.720

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0650.016
Scholarly communication0.0070.002
Open science0.0020.005
Research integrity0.0030.004
Insufficient payload (model declined to judge)0.0040.000

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 head0.054
GPT teacher head0.327
Teacher spread0.273 · 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

Citations2
Published2025
Admission routes3
Has abstractyes

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