Examining Indigenous leadership in the academy: A methodological approach
Bibliographic record
Abstract
Formal Indigenous leadership within Australian universities has expanded significantly in the last decade. Given this advancement, understanding how to integrate Indigenous leadership into existing institutional governance structures is an area that requires investigation. Recognising the need to further examine Indigenous leadership in the higher education governance structure, the Walan Mayiny: Indigenous Leadership in Higher Education project commenced in 2018. This Australian Research Council funded project specifically aims to examine the roles and subsequent responsibilities of senior Indigenous appointments within the Australian higher education sector and senior Indigenous higher education roles across Canada, New Zealand and the United States. In doing so, it investigates the responsibilities, impacts, key advantages and barriers of senior Indigenous appointments within Australian universities from an Indigenous perspective. The article provides details of the theoretical framework and research methods adopted within the Walan Mayiny: Indigenous Leadership in Higher Education project. Preliminary findings and demographic information pertaining to the participants who have currently contributed to the study will be presented in order to help us gain a better understanding of the role and subsequent value of Indigenous leadership within the higher education sector.
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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 | 0.090 | 0.094 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.012 | 0.014 |
| Science and technology studies | 0.010 | 0.011 |
| Scholarly communication | 0.009 | 0.004 |
| Open science | 0.004 | 0.009 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 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".