Bibliographic record
Abstract
In Australia, Aboriginal and Torres Strait Islander peoples; in Canada, First Nation, Métis and Inuit peoples; in Scandinavia, Sámi and in New Zealand, Maori people; have been the “subjects” of formal and informal research since colonisation. However, in many colonised countries Indigenous people have had limited opportunities to be the researchers or undertake postgraduate study by research. This book explores how Indigenous people may be better supported towards more equitable participation to undertake higher degree research postgraduate studies in higher education institutions internationally. Increasing numbers of Indigenous postgraduate students and researchers is key to enabling leaders and communities, and in the development and understanding of and respect for Indigenous histories, cultures and language within curriculum and pedagogy and approaches to research. Importantly, postgraduate students and researchers can also be agents of power and have the capacity to not only subvert and resist but to positively advance within their own context. There is an important contribution to be made by giving voice to Indigenous postgraduate students so that they can share directly the stories of their experience, their inspirations and difficulties in undertaking postgraduate study. Bringing the topic and the voices of Indigenous students clearly into the public domain provides a catalyst for discussion of the issues and potential strategies to assist future Indigenous postgraduate students and can provide sustainable solution-focused and change-focused strategies to support Indigenous postgraduate students who will go on to become stronger Indigenous educational leaders, in turn supporting the next generation.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.195 | 0.055 |
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".