The path is made by walking: knowledge, policy design and impact in Indigenous policymaking
Bibliographic record
Abstract
2020 threw into stark relief the fact that the impact of policy interventions in Indigenous affairs over the last decade and a half has been scandalously minimal. Explanations for this focus on technocratic themes such as implementation, leadership failure or lack of resources. The problem, however, is not a technical one, there is something wrong with the policy design related to Indigenous Australians. Policy design involves questions of not just what we know, but how we know, and how this knowledge is mobilized in and through policymaking. Policy impact Indigenous contexts is low precisely because contemporary policymaking excludes the knowledge and insights of Indigenous people. This makes important knowledge inaccessible to state and non-state actors, and fatally weakens policymaking. This paper appropriates the concept of metis to interrogate the root of policy failure in processes of epistemological exclusion and suppression that underpin modern statecraft is of critical importance to improving the impact of the Aboriginal and Torres Strait Islander policy enterprise. The chief contention is that improved impact in the Aboriginal and Torres Strait Islander policy enterprise hinges on centering Aboriginal metis at the epistemic, discursive, and conceptual core of the enterprise.
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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 | 0.101 | 0.073 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.016 | 0.082 |
| Scholarly communication | 0.028 | 0.023 |
| Open science | 0.003 | 0.015 |
| Research integrity | 0.008 | 0.007 |
| 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".