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Record W4391751899 · doi:10.26522/ssj.v18i1.3921

Cripping the Story of Overcoming: An Analysis of the Discourses and Practices of Self-Regulation in Early Childhood Education and Care (ECEC)

2024· article· en· W4391751899 on OpenAlexaffvenueabout
Maria Karmiris, Adam Davies

Bibliographic record

VenueStudies in Social Justice · 2024
Typearticle
Languageen
FieldPsychology
TopicEducational and Psychological Assessments
Canadian institutionsUniversity of GuelphUniversity of Toronto
Fundersnot available
KeywordsEarly childhood educationChild careChildhood educationSociologyPolitical scienceGender studiesPedagogyEconomic growthEconomicsNursingMedicine

Abstract

fetched live from OpenAlex

This paper applies crip theory (McRuer, 2006, 2018) as well as other key conceptual tools from disabled childhood studies (Runswick-Cole et al., 2018) and disability studies in education (Cousik & Maconochie, 2017) as a tactic intended to question and resist the story of overcoming as it manifests itself within the discourses and practices of self-regulation in early learning classrooms. This paper offers a brief overview of the range of self-regulation strategies enacted within educational settings in Ontario, Canada, that purport to support young children in overcoming themselves on their way to normalcy. This paper also engages in crip theory as a strategy to both question and disrupt the taken for granted assumption that self-regulation entails a return towards or a sustaining of the efficient and productive neoliberal individual in school systems. Finally, this paper considers how we might not only invite but embrace the disruptions that occur when embodied differences refuse to be overcome by demands to self-regulate. Ultimately, a key aim of this paper is to resist how discourses and practices of self-regulation in Early Childhood Education and Care (ECEC) establish the overcoming narrative as a means to cure, fix or exclude embodied differences while contemplating the vibrant possibilities embedded within learning with and from disabled childhoods.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.428
Threshold uncertainty score0.156

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 head0.061
GPT teacher head0.474
Teacher spread0.413 · 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

Citations2
Published2024
Admission routes3
Has abstractyes

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