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Record W3024790241 · doi:10.14507/epaa.28.5162

Reframing citizenship education: The shifting portrayal of citizenship in curriculum policy in the province of Ontario, 1999-2018

2020· article· en· W3024790241 on OpenAlexaffabout
Jesse K. Butler, Peter Milley

Bibliographic record

VenueEducation Policy Analysis Archives · 2020
Typearticle
Languageen
FieldSocial Sciences
TopicEducator Training and Historical Pedagogy
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsCitizenshipPoliticsIdeologyPublic administrationCurriculumSociologyState (computer science)Political sciencePublic relationsPolitical economyLaw

Abstract

fetched live from OpenAlex

State-mandated curriculum policy documents have an important political function. Governments use them to make ideological statements about the role of schools and how the next generation of citizens are to be shaped. Beginning from this premise, we use a frame analysis methodology to examine how citizenship in the Province of Ontario, Canada is framed in four consecutive versions of the curriculum policy documents that prescribe citizenship education for secondary schools. Our analysis spans 20 years, during which two political parties – one conservative, the other liberal – held power. Our inductive analysis is presented using a typology of citizenship with five dimensions: political, public, cultural, juridical, and economic. We illustrate consistency across the decades, including a preoccupation with: 1) external and internal threats to the stability and unity of Canada (political); 2) fostering nationalistic identification (political); 3) developing transferrable skills for the globalized economy (economic); 4) establishing a pre-set role for the individual citizen, characterized by legal and ethical obligations (juridical). We reveal a gradual de-emphasis of opportunities for citizens to actively participate in reshaping their communities and society (public, cultural). This shift in the political and ideological meaning of citizenship conceives citizens as isolated individuals in a reified state and society.

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)0.001
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.347
Threshold uncertainty score0.990

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.004
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
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 head0.053
GPT teacher head0.368
Teacher spread0.315 · 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.

The models applied no category: nothing in the taxonomy fit this work.
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
Published2020
Admission routes2
Has abstractyes

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