Smashing It to Bits—Risky Tactics to Change First Nations' Education
Bibliographic record
Abstract
Like the character of Neo in The Matrix , some people arrive with ideas about how to crack the code of the status quo, or at least, where to begin. This chapter follows Dr. Chris Sarra who became the first Aboriginal Principal of a small school in the Queensland town of Cherbourg. He challenged decades of neglect, and through a range of unconventional and risky tactics, together with provocative love, overturned entrenched attitudes to build an educationally and socially successful school. Chris invites us to learn from his warts and all stories. The study shows how provocation requires disciplined and persistent risky action, involving various provocative actions directing people’s attention to the hard work to be done. If for no other reason, it needs to help them see how they have colluded with and been captured by the status quo. This is the foundation for opening all our minds to alternative and better ways. This chapter outlines Chris’s journey and examines the nature of his risky moves, how he determined who and what to challenge, and shows how provocation becomes less contentious when you are committed to clear goals. We also discuss several essential ingredients for effective provocation: self-belief, persistence, flexibility, and love, which both increase and decrease the risk.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.017 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.003 | 0.009 |
| Insufficient payload (model declined to judge) | 0.012 | 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".