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Record W2249519988 · doi:10.2304/pfie.2014.12.3.403

Learning through Civic Participation: Policy Actors' Perspectives on Curriculum Reform Involvement in Ontario

2014· article· en· W2249519988 on OpenAlexaffabout
Laura Elizabeth Pinto

Bibliographic record

VenuePolicy Futures in Education · 2014
Typearticle
Languageen
FieldSocial Sciences
TopicGlobal Education and Multiculturalism
Canadian institutionsOntario Tech University
Fundersnot available
KeywordsCurriculumSociologyPublic policyPoliticsPresumptionPublic administrationPolicy analysisCoproductionCivicsEmpowermentPublic relationsEquity (law)Political sciencePedagogyLaw

Abstract

fetched live from OpenAlex

When citizens participate in policy production, the advantages go beyond policy outcomes — though the presumption is that participation leads to better public policy. Robust democracy characterized by agonistic exchanges among policy actors ought to encourage learning, dialogue, empowerment, equity, and a shared spirit of inquiry. This article describes and analyses citizen participation in curriculum policy production during the late 1990s based on archival documents and interviews with 16 policy actors (including writers, bureaucrats, and consultation participants). Their reflections on the process reveal unintended learning about politics, government, and the cultivation of civic skills arising out of interaction with others in different roles and on different ends of the political spectrum. While the analysis reveals how some describe the process as ‘radicalizing’ enlightenment, the findings point to a number of areas for improving opportunities for learning in formal policy production processes, as well as areas for further empirical investigation.

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)0.000
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation 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 score0.315
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.026
GPT teacher head0.390
Teacher spread0.364 · 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 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

Citations4
Published2014
Admission routes2
Has abstractyes

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