What is it like to be a child? Childhood subjectivity and teacher memories as heterotopia
Bibliographic record
Abstract
Foucault's notion of heterotopia offers a novel way to understand teachers’ conceptualizations of childhood, in juxtaposing adult memories of childhood with their present context of teaching children. Memory writing prompts were given to 41 early childhood teachers, and the resulting written narratives were analyzed as heterotopic spaces. The study follows two trajectories. First, in terms of teacher development, we examine how the construct of heterotopia can help teachers and teacher educators understand the impact of memories on their current assumptions about childhood. Second, we argue that examining the teacher's internal experiences through heterotopia can contribute to theoretical thinking about childhood. The study's findings suggest that it is a considerable but meaningful challenge to examine our subjective experience of childhood in relation to our understanding of children today. This process may be useful in assisting the teacher to disentangle the imagined, remembered, conceptualized and actual child, and to interrupt our tendencies to project our own experiences onto others. Perhaps, there is a childhood that exists in heterotopic spaces, not quite the subjective or psychical child, but not quite the external child either. This may be the liminal childhood that the early childhood teacher, preoccupied as (s)he is with childhood, experiences. Theorizing teachers’ subjectivities as they are linked to their memories of childhood is a complex endeavour, and Foucault's heterotopia provides rich images of strange juxtaposition that may be useful in thinking about childhood and teaching.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.021 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.003 |
| 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".