Creating confirming classrooms: Indigenous expertise in inclusive pedagogies and policy transformation
Bibliographic record
Abstract
Across Australia, Canada, Aotearoa (New Zealand), and the United States, Indigenous students continue to face racism, stereotyping, and systemic exclusion within schooling systems. These experiences contribute to widespread disengagement, anxiety, and educational underachievement. While teachers are tasked with fostering safe and inclusive classrooms, many lack the cultural knowledge and critical reflexivity required to address these harms effectively. This study adopts an Indigenous phenomenological approach to explore how sixteen Indigenous educational scholars understand and enact inclusive, culturally responsive pedagogy. Participants were selected based on their identification as Indigenous and their expertise in curriculum, teacher education, and policy. Although informed by international perspectives, the findings are primarily situated within the Australian context. Through the analysis of expert testimony, the study identifies key strategies for transformation, including the development of critical reflexivity, the use of storytelling as relational pedagogy, culturally accountable role-modelling, and policy frameworks that enable systemic change. These elements are positioned as essential to countering educational inequities and embedding Indigenous knowledges and values at the heart of practice. By foregrounding Indigenous voices, this study contributes to a relational, decolonial framework for educational renewal – one that benefits not only Indigenous learners, but all students in increasingly diverse educational settings.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.023 | 0.027 |
| Scholarly communication | 0.009 | 0.009 |
| Open science | 0.002 | 0.019 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".