Maddening pre-service early childhood education and care through poetics: Dismantling epistemic injustice through mad autobiographical poetics
Bibliographic record
Abstract
In this article, the author forwards the importance of mad autobiographical poetic writing to challenge and disrupt epistemic injustice within pre-service early childhood education and care. They explore their own mad autobiographical poetic writing as a queer, non-binary, mad early childhood educator and pre-service early childhood education and care faculty member, and argue that mad poetic writing can methodologically be used as a form of resistance to epistemic injustices and epistemological erasure in early childhood education and care. This article argues for the importance of autobiographical writing in early childhood education and care, and the necessity of centralizing early childhood educators' subjectivities and histories when addressing - and transforming - issues of equity, inclusion and belonging in early childhood education and care. The personal and intimate mad autobiographical poetic writing of this article - written by the author - focuses on how personal experience with madness as it pertains to working within pre-service early childhood education and care can challenge norms that govern and regulate madness. Ultimately, the author argues that transformation in early childhood education and care can take place by reflecting on experiences of mental and emotional distress, and considering poetic writings as starting places for imagining new futurities and a plurality of educator voices and perspectives.
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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 | 0.009 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.008 | 0.064 |
| Scholarly communication | 0.012 | 0.009 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.002 | 0.006 |
| Insufficient payload (model declined to judge) | 0.003 | 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".