Elements of Country: a First Nations-first approach to chemistry
Bibliographic record
Abstract
Abstract Collectively, we have chosen to explore an Australian First Nations-first approach to understanding the chemical elements. We believe that engagement with cultural heritage, ongoing cultures, and the knowledges ofthis place—the lands on which we work, live, and study—will lead to new ways of understanding the elements and change the way we practice chemistry. The “First Nations first” phrase and approach comes from understanding the unique place that Aboriginal and Torres Strait Islander peoples have in the Australian context. In this paper we explore how a First Nations-first approach could take place in Sydney on Aboriginal lands. This approach is led by Aboriginal people, engages with culture, and is produced with local knowledge holders. So far, the work has entailed two years of meeting, conversing, and sharing space to determine appropriate ways of working together, interrogating the complexities of the ideas, and to refining our approach to the work. To appreciate the significant shift that a First Nations-first approach represents for chemistry, we consider the legacy of the Periodic Table. We share some reflections on how Indigenous knowledges can contribute to an expanded chemistry curriculum through the recognition of productive cultural tension.
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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 | 0.010 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.012 | 0.025 |
| Scholarly communication | 0.010 | 0.008 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.002 | 0.008 |
| 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".