Bibliographic record
Abstract
This paper explores how whiteness scholarship can support deep engagement with both historical and contemporary forms of whiteness and racism in early childhood education. To this point, the uptake of whiteness scholarship in the field of early childhood has focused predominantly on autobiographical narratives. These narratives recount white educators’ stories of ‘becoming aware’ or ‘unmasking’ their whiteness. In colonising contexts including Australia, New Zealand and Canada, understanding how whiteness operates in different ways and what this means for educational research and practice, can support researchers and educators to identify and describe more fully the impacts of subtle forms of racism in their everyday practices. In this paper, whiteness is explored in a broader sense as: a form of property; an organising principle for institutional behaviours and practices; and as a fluid identity or subject position. These three intersecting elements of whiteness are drawn on to analyse data from a doctoral study about embedding Aboriginal and Torres Strait Islander perspectives in early childhood education curricula in two Australian urban childcare settings. Analysis is focused on how whiteness operated within the research site and research processes, along with the actions, inaction and talk of two educators engaged in embedding work. Findings show that both the researcher and educators reinforced, rather than reduced the impacts of whiteness and racism, despite the best of intentions.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.017 | 0.035 |
| Scholarly communication | 0.009 | 0.006 |
| Open science | 0.001 | 0.013 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 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".