<b>Caring for Country:</b><b><i>Murnong, Murnong, growing strong: A window mural </i></b>Created by primary school children and Monash pre-service teachers as inspired by First Nations Artists and Cultural Knowledge Holders.
Bibliographic record
Abstract
Murnong, murnong, make it strong. 2024 Aunty Karan Kent, Kylie Colemane, Nikki Browne, Geraldine Burke and Melissa Bedford with Monash Faculty of Education and Dandenong Primary School students Vinyl installation commissioned by Monash Faculty of Education, Monash University Museum of Art | MUMA and Dandenong Primary School Design: Chris Hanger Photography: Andrew CurtisImages featured 1. Detail of window mural design in progress 2. Detail of window mural with shadow casts3. Detail of window mural with murnong and Wayapa-inspired patterns 4. Detail of window mural with bandicoot and possum5. Detail of window mural with Wayapa-inspired patterns and murnong 6. Detail of window mural with wall text This window mural connects us to Country while helping us care for murnong. It is also about growing strong with First Nations artists and knowledge holders, Aunty Karan Kent, Bidjara, Kylie Colemane, Darug, Wiradjuri and Nikki Browne, Bidjara.Our window mural acknowledgements: This window mural was created by the talented DPS children, as supported by their enthusiastic teachers, especially Keery Gibson and Sara Djakovic that was co-created with Monash pre-service teachers. Importantly the mural draws inspiration from First Nations cultural knowledge.We thank Aunty Karan for inspiring the children’s drawings through Wayapa Wuurrk movement and stories of Indigenous connections to Country.We are grateful to Kylie Coleman for sharing extensive cultural knowledge about plants and animals, especially the murnong daisy and its importance as an important food source.Thanks also go to Nikki Browne for cultural liaison and the of stories that have broadened our understanding of connections to Country and the role of Bunjil and Waa.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.266 | 0.074 |
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".