The ATSIC Aboriginal Community Development Planning Program in northern Australia : approaches and agendas
Bibliographic record
Abstract
There are so many people to thank that it is hard to know where to begin, so I will first offer my apologies to all those who are not mentioned by name, but who have assisted in the development of these papers by giving so generously of their materials, their time and, most importantly, of their ideas and thoughts.Across northern Australia people in many Aboriginal communities and organisations have offered their ideas, their comments and their guidance over the years that I have been interested in community and area planning and development issues.The staff of the ATSIC regional offices generously assisted with their files, their recollections and observations, as did those from DEET regional offices.In particular the staff of the Cairns A TSIC Regional Office, and members of the Peninsula, and Cairns and Region, Regional Councils, have given unstintingly of their experience and their ideas.Bill Sheldon, community and regional planner, and Geoff Richardson, regional manager at Cairns have provided assistance, new insights, critiques, and support.Staff of the ATSIC central office Regional Support Branch, which sponsored the initial report, have been very helpful with travel and other arrangements.Special thanks to branch head Shane
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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 | 0.006 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.008 | 0.003 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.020 | 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".