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Record W2320655626 · doi:10.1386/ctl.9.3.297_1

Mapping the civic education policy community in Canada: A study of policy actors’ attitudes

2014· article· en· W2320655626 on OpenAlexaffabout
Stephanie Bell, Jeff Lewis

Bibliographic record

VenueCitizenship Teaching and Learning · 2014
Typearticle
Languageen
FieldSocial Sciences
TopicEducator Training and Historical Pedagogy
Canadian institutionsUniversity of New BrunswickYork University
Fundersnot available
KeywordsPoliticsPublic administrationPublic policyGovernment (linguistics)Education policyPolitical scienceDisengagement theoryPolicy studiesHigher educationLaw

Abstract

fetched live from OpenAlex

Abstract Although civic education in Canada is typically seen as the responsibility of the provincial public school system and despite the fact that youth disengagement is widely accepted as a problem, civic education is a policy area that does not receive sustained attention from either the public or government. However, attention to the problem of political apathy and ignorance in Canada continues to grow and the number of policy actors in both governmental and non-governmental settings is increasing. The growing civic education policy network and community is occurring in a vacuum of policy ambiguity and ambivalence. In an attempt to better understand the civic education policy network in Canada we surveyed both federal and provincial government and non-governmental actors. In our survey, we asked policy actors to rank other policy actors in terms of collaboration, trust, influence and reliance in the policy network. In addition to this we asked the actors about their attitudes on the policy outcomes of civic education policy in relation to political behaviour and political knowledge. Our findings suggest that the policy network is highly centralized with federal government actors and a handful of national non-government actors. Also, we found that civic education policy actors in Canada generally agree on both political knowledge and political behaviour policy outcomes.

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)0.004
metaresearch head score (Gemma)0.008
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Science and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.045
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.008
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0030.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.002
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 head0.109
GPT teacher head0.377
Teacher spread0.268 · 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 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
Published2014
Admission routes2
Has abstractyes

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