Shifting the terrain, enriching the academy: Indigenous PhD scholars’ experiences of and impact on higher education
Bibliographic record
Abstract
Abstract In Australia, much like other colonized locations such as Canada, New Zealand, and the USA, the colonial legacies embedded within higher education institutions, including the history of exclusion and the privileging of Western epistemologies, continue to make universities challenging places for Indigenous PhD scholars. Despite this, and while the numbers of Indigenous PhD scholars remain well below population parity, they are carving a space within the academy that is shifting the academic terrain and enriching the research process. Drawing on in-depth interviews with Indigenous PhD scholars working in the field of health and a qualitative survey of doctoral Supervisors and Advisory Committee Chairs, this paper explores the doctoral experience of Indigenous scholars. What becomes apparent, through this research, is that despite ongoing experiences of racism and alienation, these scholars are finding ways to circumvent inadequate supervisory processes, systems support, and research paradigms, to carve a path that centers Indigenous ways of knowing, being, and doing.
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 | 0.023 | 0.027 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.032 | 0.039 |
| Scholarly communication | 0.017 | 0.009 |
| Open science | 0.002 | 0.023 |
| Research integrity | 0.004 | 0.008 |
| 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".