Factors impacting non-Indigenous educators’ inclusion of First Nations’ content in schools: A systematic literature review
Bibliographic record
Abstract
Abstract This systematic review is a rigorous analysis of Australian literature that identifies the factors (barriers and enablers) that impact non-Indigenous educators’ inclusion of First Nations' content in Australian schooling. Understanding the barriers and enablers to pedagogical inclusion are an important elements to upholding the various governmental policies and redressing education inequities in the settler-colonial Australian context. Key databases, specifically Informit, Web of Science, ProQuest, and Google Scholar were searched which resulted in n = 40 articles deemed to meet the in/exclusion criteria. Inductive thematic synthesis was conducted to provide valuable insights about the relevant barriers and enablers for non-Indigenous educators to include First Nations content in their practice. Findings indicate that lack of knowledge and prioritisation for First Nations content were significant epistemic barriers with standpoint and confidence identified as affective barriers. Enablers were identified as relationships with First Nations Peoples to inform shifts in pedagogical lens, as well as ongoing professional development and support. The results of this systematic review suggest that redressing the current state of Australian education requires tackling of multifaceted, complex, and interrelated factors to facilitating sustainable change so that all students have the opportunity to develop social-justice worldviews associated with valuing and truth-telling.
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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 | 0.037 | 0.133 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.017 | 0.015 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| 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".