Learning to care for Dangaba
Bibliographic record
Abstract
Abstract In a Kimberley place-based cultural story, Dangaba is a woman whose Country holds poison gas. Her story shows the importance of cultural ways of understanding and caring for Country, especially hazardous places. The authors contrast this with a corporate story of fossil fuel, illustrating the divergent discourses and approaches to place. Indigenous and local peoples and their knowledge, cultures, laws, philosophies and practices are vitally important to Indigenous lifeways and livelihoods, and critically significant to the long-term health and well-being of people and place in our locality, region and world. We call for storying and narratives from the pluriverse of sociocultural voices to be a meaningful part of environmental education and to be implemented in multiple places of learning. To know how to hear, understand and apply the learnings from place-based story is to know how to move beyond a normalised worldview of separation, alienation, individualism, infinite growth, consumption, extraction, commodification and craving. To know how to see, feel, describe and reflect upon experience, concepts and practice is to find ways to move towards radical generosity, mutuality of becoming, embodied kinship, wisdom, humility and respect.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.020 | 0.008 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.002 | 0.012 |
| Research integrity | 0.003 | 0.008 |
| Insufficient payload (model declined to judge) | 0.016 | 0.003 |
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".