Cripping the Story of Overcoming: An Analysis of the Discourses and Practices of Self-Regulation in Early Childhood Education and Care (ECEC)
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.010 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.021 | 0.057 |
| Scholarly communication | 0.013 | 0.008 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".