Indigenous Postgraduate Education: Intercultural Perspectives
Bibliographic record
Abstract
This book focuses on Indigenous participation in postgraduate education. The collaborating editors, from the contexts of Australian, Canadian and Nordic postgraduate education, have brought together voices of Indigenous postgraduate students and researchers about strategies to support postgraduate education for Indigenous students globally and to promote sustainable solution-focused and change-focused strategies to support Indigenous postgraduate students. The role of higher education institutions in meeting the needs of Indigenous students is considered by contributing scholars, including issues related to postgraduate education pedagogies, flexible learning and technologies. On a more fundamental level the book provides a valuable resource by giving voice to Indigenous postgraduate students themselves who share directly the stories of their experience, their inspirations and difficulties in undertaking postgraduate study. This component of the book gives precedence to the issues most relevant and important to students themselves for consideration by universities and researchers. 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. This book will assist higher education providers to develop understanding of how Indigenous postgraduate students and researchers negotiate research cultures and agendas that permeate higher education from the past to ensure the experience of postgraduate students is both rich in regard to data to be collected and culturally safe in approach; what connections, gaps and contradictions occur at the intersections between past models of postgraduate study and emerging theories around intercultural perspectives, including the impact of cultural and linguistic differences on Indigenous students’ learning experiences; how Indigenous students’ and researchers’ personal and professional understandings, beliefs and experiences about what typifies knowledge and research or adds value to postgraduate studies are constructed, shared or challenged; and how higher education institutions manage the potential challenges and risks of developing pedagogies to ensure that they give voice and power to Indigenous postgraduate students.
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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 | 0.006 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.024 | 0.026 |
| Scholarly communication | 0.011 | 0.008 |
| Open science | 0.002 | 0.013 |
| Research integrity | 0.005 | 0.012 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".