Co-designing for First Nations leadership in land management: listening to stories, designing for experience, and advocating for change
Bibliographic record
Abstract
In this study, research through design (RtD) and the practices of Indigenous-led co-design provide platforms to empower active participation in designing to strengthen the recognition and respect for Indigenous leadership in contemporary land management. Initially, we describe and document the project context, the design outcomes and the co-design processes that contribute to the design outcomes for the Indigenous project ‘Healing People, Healing Country: Cultural Fire Storytelling and Education’ in the Hunter Valley in Australia. These material outcomes include templates for co-design processes, the co-design of interpretive signs, a publication and two suites of videos. Secondly, the researchers engage in a reflexive analysis of the outcomes and processes undertaken during the project. Both these research pieces aim to contribute to developing effective co-design platforms to ensure a plurality of voices in storytelling. The focus of this re-telling is to understand and share what has been learnt in this Indigenous project as a contribution to the complex work of designing meaningful ways to support Indigenous sovereignty and self-determination in land-based projects situated in urban contexts.
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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 | 0.028 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.015 | 0.030 |
| Scholarly communication | 0.013 | 0.009 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 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".