Stop ‘under-mind-ing’ early childhood educators: Honouring subjectivity in pre-service education to build intellectual and relational capacities
Bibliographic record
Abstract
The stubborn dominance of objectivity in child observation in pre-service early childhood education warrants letting go of as we confront its limitations as outdated, problematic, Eurocentric, neo-liberal and even racist. In the context of recent aims to establish ‘critically reflective’ practices, such as ‘pedagogical documentation’ and ‘collaborative inquiry’ as the ‘new way’ to ‘do’ early childhood curriculum planning in Ontario, Canada, the authors are concerned that the hard work of naming and creating conditions to ‘think together’ with concepts of subjectivity has been missed and misunderstood. The risk of missing this shared thinking and not persevering in the struggles of subjectivities, especially in curriculum courses and placement, underestimates and ‘under-minds’ the intellectual capacity of students and positions theory as neutral in its relation to practice. How, then, does one take up subjectivity and recognize its affordance in building the intellectual and relational capacity of pre-service students? What conditions need to be created to lead with critical thinking and engage in subjectivities in the context of early childhood education pre-service programs? Drawing on critical educational perspectives, the authors work to define subjectivity in the context of early childhood education; identify the conceptual barriers that they have encountered in their work as a professor and a field liaison; and propose potentially generative conditions for pre-service programs.
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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 | 0.014 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.015 | 0.082 |
| Scholarly communication | 0.012 | 0.008 |
| Open science | 0.002 | 0.013 |
| Research integrity | 0.003 | 0.008 |
| 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".