Leading from Between: Indigenous Participation and Leadership in the Public Service
Bibliographic record
Abstract
Since the 1970s governments in Canada and Australia have introduced policies designed to recruit Indigenous people into public services. Today, there are thousands of Indigenous public servants in these countries, and hundreds in senior roles. Their presence raises numerous questions: How do Indigenous people experience public-sector employment? What perspectives do they bring to it? And how does Indigenous leadership enhance public policy making? A comparative study of Indigenous public servants in British Columbia and Queensland, Leading from Between addresses critical concerns about leadership, difference, and public service. Centring the voices, personal experiences, and understandings of Indigenous public servants, this book uses their stories and testimony to explore how Indigenous participation and leadership change the way policies are made. Articulating a new understanding of leadership and what it could mean in contemporary public service, Catherine Althaus and Ciaran O'Faircheallaigh challenge the public service sector to work towards a more personalized and responsive bureaucracy. At a time when Canada and Australia seek to advance reconciliation and self-determination agendas, Leading from Between shows how public servants who straddle the worlds of Western bureaucracy and Indigenous communities are key to helping governments meet the opportunities and challenges of growing diversity.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".