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Record W4210830042 · doi:10.14507/epaa.30.7002

Shifting meanings: The struggle over public funding of private schools in Alberta, Canada

2022· article· en· W4210830042 on OpenAlexaffabout
Sue Winton, Steven Staples

Bibliographic record

VenueEducation Policy Analysis Archives · 2022
Typearticle
Languageen
FieldDecision Sciences
TopicEvaluation and Performance Assessment
Canadian institutionsYork University
Fundersnot available
KeywordsArgumentativeNeoliberalism (international relations)Dominance (genetics)Discourse analysisPublic administrationPolicy analysisGovernment (linguistics)SociologyPublic policyEducation policySchool choicePolitical sciencePolitical economyHigher educationLaw

Abstract

fetched live from OpenAlex

The government of Alberta, Canada, has provided public funding to eligible private schools since 1967. This policy has always been contested, and in this article, we explain how we applied concepts from argumentative discourse theory and its attendant methodology, argumentative discourse analysis (ADA), to trace the debate over the policy since 1990. Argumentative discourse theory posits that policymaking involves struggles for discursive dominance wherein actors try to convince others to view the policy issue in a particular way. Drawing on 158 media articles, interviews, and secondary sources, we show that although some of the actors in the dispute have changed – and changed sides – their arguments have remained fairly consistent. However, their arguments’ meanings – and of the policy itself – have changed as the dominant discourse in the province shifted. Specifically, in response to the rise and predominance of neoliberalism in Alberta, supporters redefined the policy to fund private schools with public money as one that promoted choice and competition that would improve schooling. Opponents, on the other hand, recast the policy as part of a larger government effort to privatize public education. We demonstrate that argumentative discourse theory and ADA can be used to support the goals of critical policy analysis.

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)0.002
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.153
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.005
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.000

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 head0.087
GPT teacher head0.435
Teacher spread0.348 · 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 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

Citations5
Published2022
Admission routes2
Has abstractyes

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