Molecular Decolonization: An Indigenous Microcosm Perspective of Planetary Health
Bibliographic record
Abstract
Indigenous peoples are resilient peoples with deep traditional knowledge and scientific thought spanning millennia. Global discourse on climate change however has identified Indigenous populations as being a highly vulnerable group due to the habitation in regions undergoing rapid change, and the disproportionate burden of morbidity and mortality already faced by this population. Therefore, the need for Indigenous self-determination and the formal recognition of Indigenous knowledges, including micro-level molecular and microbial knowledges, as a critical foundation for planetary health is in urgent need. Through the process of Indigenous decolonization, even at the smallest molecular scale, we define a method back to our original selves and therefore to our planetary origin story. Our health and well-being is directly reflected at the planetary scale, and we suggest, can be rooted through the concept of molecular decolonization, which through the English language emerged from the 'First 1000 Days Australia' and otherwise collectively synthesized globally. It is through our evolving understanding of decolonization at a molecular level, which many of our Indigenous cultural and healing practices subtly embody, that we are better able to translate the intricacies within the current Indigenous scientific worldview through Western forms of discourse.
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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 | 0.004 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.066 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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".