Bibliographic record
Abstract
For the first time in Australia a local council has used a deliberative democracy approach to obtain citizen advice on key decisions regarding the full range of Council services, service levels and funding. Typically a participatory budget (PB) gives citizens authority in relation to a component of the local government budget. The City of Canada Bay Council, in metropolitan Sydney, went well beyond this. In this paper the Canada Bay Citizens’ Panel (CP), the name given to the PB, is compared to the traditional PB process highlighting three distinctive features of this process: (1) the use of a randomly selected group of citizens; (2) the role of the newDemocracy Foundation as a ‘nonpartisan intermediary organisation’ (Kadlec and Friedman, 2007); and (3) the engagement of council staff through a parallel process convened by the Council, using a randomly selected staff panel. Whilst it is too early yet to make any final judgments, there is promising evidence that the recommendations of this CP will be seriously considered and that this engagement model will be used again by the City of Canada Bay, for the next four-year delivery plan and other contentious issues. Even though the Canada Bay Citizens’ Panel process is not yet complete, it is already clear that its impact will be felt, not only on the budget of the City of Canada Bay, but more broadly as an exemplar for local governments in Australia thinking about engaging their citizens.
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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 | 0.045 | 0.052 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.019 | 0.017 |
| Scholarly communication | 0.017 | 0.010 |
| Open science | 0.002 | 0.022 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.015 | 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".