Walking together: Artistic collaboration across cultures in Australia and New Zealand
Bibliographic record
Abstract
As an artist and writer who often works across disciplines and cultures, my education into effective and respectful engagement has been built on my experience working with First Nations friends, collaborators, and Elders. The aim of this paper is to explore teachings from a number of these leading thinkers, writers, and Elders on the topic of knowledge sharing, cross-cultural awareness, and ethical engagement through practice-led research. Drawing from personal experience, it will incorporate learnings that have informed a world view that has been evolving since childhood. The paper highlights the importance of giving rightful recognition to knowledge keepers and provides some guidance for readers interested in developing productive and respectful partnerships with First Nations collaborators. Here knowledge can be safely shared and celebrated as ways to understand the world around us that are restorative and regenerative. I speak as a woman of mixed European background raised in Australia on Gubbi Gubbi Country of South East Queensland, and Larrakia Country of Darwin. Culturally, I am descended from Norse, Celt, Saxon, and Druid ancestors. Through this lived experience I hope to share learnings that support the goals of reconciliation, truth telling, and First Nations determination in my home country, as well as facilitating greater awareness for people seeking to respectfully engage with Indigenous knowledge.
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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 | 0.015 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.034 | 0.025 |
| Scholarly communication | 0.013 | 0.007 |
| Open science | 0.003 | 0.023 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.008 | 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".