Statistics for Community Governance: The Yawuru Indigenous Population Survey, Western Australia
Bibliographic record
Abstract
This article presents a case study of an exercise in Aboriginal community governance in Australia. It sets out the background events that led the Yawuru Native Title Holders Aboriginal Corporation in the town of Broome on Australia’s northwest coast to secure information for its own needs as an act of self-determination and essential governance, and it presents some of the key findings from that exercise. As the Indigenous rights agenda shifts from the pursuit of restitution to the management and implementation of benefits, those with proprietary rights are finding it increasingly necessary to build internal capacity for post-native title governance and community planning, including in the area of information retrieval and application. As an incorporated land-holding group, the Yawuru people of Broome are amongst the first in Australia to move in this area of information gathering, certainly in terms of the degree of local control, participation, and conceptual thinking around the logistics and rationale for such an exercise. An innovative addition has been the incorporation of survey output data into a Geographic Information System to provide for spatial analysis and a decision support mechanism for local community planning. In launching and administering the "Knowing our Community" household survey in Broome, the Yawuru have set a precedent in the acquisition and application of demographic information for internal planning and community development in the post-native title determination era.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".