Reviewing educational conceptualisations of transnational settler ignorance
Bibliographic record
Abstract
This paper investigates how majority societies’ common ignorance about Indigenous peoples and ongoing settler-colonial reality (“settler ignorance”) has been negotiated in the educational sciences literature. Understanding settler ignorance not as a simple “lack of knowledge” but a powerful issue undermining Indigenous rights and decolonial aspirations, this review sets out to gain new understanding of its dimensions in educational settings. The reviewed literature covers 51 peer-reviewed qualitative records from six settler-colonial contexts – Finland, Sweden, Norway, Canada, Australia, and Aotearoa/New Zealand. The emerging conceptualisations of the phenomenon of settler ignorance and ways of addressing it were explored through thematic synthesis. The findings suggest that settler ignorance has many faces: it is conceptualised as emotionally and ideologically contested knowledge-making, as wilful avoidance and resistance, and as a structural mechanism that transcends the question of individual cognition. Similarly, the proposed approaches to dismantling ignorance are diverse, emphasising the potential of educational content, building relationality, and critical reflection. Discussing the findings’ implications, the article suggests how harnessing both context-based and transnational understandings about settler ignorance and its many dimensions could benefit reconciliatory processes between settler and Indigenous populations and signpost one approach to decolonising education.
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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 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.009 | 0.010 |
| Science and technology studies | 0.002 | 0.009 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.004 |
| 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".