Walking With Place: Storying Reconciliation Pedagogies in Early Childhood Education
Bibliographic record
Abstract
Knowing and understanding the land with Aboriginal cosmologies requires seeing much deeper than the surface. It involves feeling those deep connections that have existed for thousands of years and understanding trees, rocks, and rivers. Drawing on Vanessa Watt’s concept of place-‐thought and Latour’s emerging common world framework, I explore the notion of country in a specific place in the Australian context. This paper pays attention to the stories of Australia’s colonial pasts, presents, and futures as I set out to generate new reconciliation pedagogies and engage with place during an experiential learning exercise: place-‐thought-‐walk. I argue that place-‐thought pedagogies that are inclusive, respectful, and reconciled to people of the local Aboriginal group can be put to work as a decolonizing practice. This practice exposes the layers of colonial inscription in the landscape, creating space for the land to be reclaimed and reinscribed with Aboriginal knowledges as the central frame.
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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 | 0.008 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.019 |
| Scholarly communication | 0.006 | 0.009 |
| Open science | 0.002 | 0.016 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 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".