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Record W3107437314 · doi:10.33524/cjar.v21i1.476

Masquerade of Neoliberal Concepts Revealed: Self-Regulation Skills in Ontario’s Full Day Kindergarten Program

2020· article· en· W3107437314 on OpenAlexaffvenueabout
Manu Sharma

Bibliographic record

VenueThe Canadian Journal of Action Research · 2020
Typearticle
Languageen
FieldSocial Sciences
TopicTeacher Education and Leadership Studies
Canadian institutionsThompson Rivers University
Fundersnot available
KeywordsAction researchAction (physics)PsychologyPedagogyNeoliberalism (international relations)Argument (complex analysis)PermissionMathematics educationSociologyPolitical scienceSocial scienceLawMedicine

Abstract

fetched live from OpenAlex

This study examines an action research project that included four kindergarten teachers who taught in the first year of the Ontario’s mandated full day kindergarten (FDK) program, which began in September 2016. This action research project focused on the teachers’ concerns about the constant unsafe violence, bullying and disruptive behaviour that characterized their FDK classrooms, which led them to explore self-regulation skills. According to the FDK program developers and school administrators, self-regulation was the key to eliminating such difficult behaviours and actions. However, the findings of the action research study revealed there were many challenges in sustaining and having students understand self-regulation, and there was a disconnect between the theoretical understanding of self-regulation and the practical reality of how self-regulation was used in the classroom. The findings of this action research study bring forth an interesting argument: the practical use of self-regulation in FDK classrooms (un)consciously gives permission to the reach and subsequent impact of neoliberalism in schools.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.005
metaresearch head score (Gemma)0.005
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.234
Threshold uncertainty score0.470

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0110.013
Scholarly communication0.0040.001
Open science0.0010.004
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0020.000

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 head0.305
GPT teacher head0.477
Teacher spread0.172 · 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 source (direct Gemma or distilled Codex), 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

Citations2
Published2020
Admission routes3
Has abstractyes

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