Developing a Local Cultural Indicator Framework in Australia: A Case Study of the City of Whittlesea
Bibliographic record
Abstract
This paper critically examines the cultural planning agenda of the City of Whittlesea, a local government municipality in Australia, and considers its impact on the region‟s multicultural communities. Located on the metropolitan fringe, the City is geographically one of the largest and most diverse municipalities in greater Melbourne, with more than half of the residents from non-Anglo-Celtic backgrounds. First, the paper shows how sustainability is achieved through a structure of inter-departmental collaboration as well as in a cultural planning focus on community cultural development. Next, it examines how sustainability is implemented in its policies and programs through the development of cultural citizenship. Finally, it evaluates two community events to consider the extent of cultural participation. Combining empirical data and theoretical research, this paper aims to produce a working model for developing local cultural indicators to measure the cultural participation of non-Anglo Celtic communities. Specifically, this paper hopes to establish cultural indicators with direct policy relevance for local government, and incorporate a detailed consideration of the „use-context‟ of the cultural indicators in the City in order to provide a template for best practice at municipal program levels. A localized cultural indicator framework will enable robust tools of measurement to account for thick narratives of multicultural participation that can continue to enhance well-being, place making, and belonging.
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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 | 0.007 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.012 | 0.007 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".