Reconceptualizing early childhood education: Cartographic relational stories
Bibliographic record
Abstract
In this article, we enact a partial cartographic storying of reconceptualist turns in our work. We do this by situating ourselves in relation to each other and our work across time as a mode of tracing the (situated) possibilities that these turns have enacted for children-in-relation with worlds. In enacting this dialogic and cartographic storying, we collaborate in a way that is inspired by Black methodologies. This means that we intentionally think with liberatory possibilities in our work in early childhood education research and practice across modalities, disciplines, temporalities and geographies. Importantly, like Black methodologies this co-theorizing is also ontological; it is inseparable from our own relational becomings. Following an anticolonial ethos, we are interested in the liberatory potentials of our work, at multiple scales and in different but specific places. We attempt to enact the difficult task of (re)storying our childhood education research and practice in ways that pay attention to the interconnected presences and effects of white supremacy, human supremacy and colonialism, while simultaneously refusing to reinscribe a flattened damage-centred understanding of children, educators and their relational worlds.
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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 | 0.016 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.013 | 0.066 |
| Scholarly communication | 0.017 | 0.025 |
| Open science | 0.003 | 0.018 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".