Transforming schooling practices for First Nations learners: culturally nourishing schooling in conversation with the theory of practice architectures
Bibliographic record
Abstract
The Australian education system is culpable in perpetuating, rather than alleviating, inequitable outcomes for First Nations peoples. To address this, the Culturally nourishing schooling project (2020-2024) involves eight high schools committed to whole-of-school change in four intertwined domains: learning from Country, culture/language programs, epistemic mentoring, and sustained professional learning. In this paper we envision how and why the theory of practice architectures (TPA) may provide a framework for understanding what happens as schools pursue this transformation. We critically examine whether TPA can provide an epistemologically and ontologically appropriate methodology to support change in schools with significant cohorts of First Nations students. A key premise of TPA is to uncover the meanings and impacts of the practices of the people entangled in school sites, and reveal the usually unseen structural arrangements that allow these practices to unfold. We contend that by making these arrangements visible, those involved in schooling are enabled to contribute to the transformative change that will foster culturally nourishing practices.
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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 | 0.024 | 0.020 |
| 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 | 0.070 |
| Scholarly communication | 0.010 | 0.011 |
| Open science | 0.002 | 0.013 |
| Research integrity | 0.003 | 0.007 |
| 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".