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Record W1664389380 · doi:10.22230/ijepl.2015v10n1a454

Democratic Dialogue as a Process to Inform Public Policy

2015· article· en· W1664389380 on OpenAlexvenueaboutno aff
D. M. Smith, Jessica Qua-Hiansen

Bibliographic record

VenueInternational Journal of Education Policy and Leadership · 2015
Typearticle
Languageen
FieldPsychology
TopicEducational and Psychological Assessments
Canadian institutionsnot available
Fundersnot available
KeywordsDialogicTransformative learningNarrativeOfficerDemocracyPublic relationsPedagogySociologyPolitical scienceProcess (computing)Public policyLaw

Abstract

fetched live from OpenAlex

An exploration of the collaborative reconceptualization of a provincial Supervisory Officer’s Qualification Program (SOQP) through the use of dialogic approaches is the focus of this inquiry. The stories, perspectives, and lived experiences of supervisory officers, principals, teachers, parents, students, and members of the public in Ontario were included as essential voices and information sources within policy development conversations. These narratives of experience revealed the forms of knowledge, skills, dispositions, and ethical commitments necessary for effective supervisory officers today and in the future. They also illustrated the transformative nature of narrative dialogue to enlighten, deepen understanding, and alter perspectives. The policy development processes used in this publicly shared educational initiative serve as a model of democratic dialogue. The inclusive and dialogic methods employed to collectively reconceptualize a supervisory officer formation program illustrate an innovative framework for developing policies governing the public good.

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: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.424
Threshold uncertainty score0.495

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
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 head0.313
GPT teacher head0.500
Teacher spread0.187 · 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 designTheoretical or conceptual
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

Citations3
Published2015
Admission routes2
Has abstractyes

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