Merging Indigenous and Western research methodologies:Reflections on a journey
Bibliographic record
Abstract
We would like to acknowledge the traditional custodians of Country across Australia and the Torres Strait Islands. Indigenous knowledges are and have been used to support us to sustainably exist with Australia’s fragile ecology for thousands of years but are only recently being valued for their role in creating a sustainable future for Australian fauna. Indigenous Ecological Knowledges can play a vital role in the future management, and recovery of Australian native species. But the value of this knowledge needs to be recognised by those in decision-making roles. Here, I present these concepts using my family totem, the Koala, as a case study for how these two knowledge systems can be merged. As part of my Honours research year, I completed reflections that were centred around the experience and challenges that I, as an Indigenous person, would experience when merging Indigenous and Western research methodologies. The key reoccurring findings of my reflections were categorised into 1) my growth as an Indigenous person, 2) gaining a deeper sense of ecology, 3) Indigenous Ecological Knowledge, and 4) incorporating culture into a Western science system. This experience overall showed that it is possible to bring your own cultural experience and way of conducting science into the current dominant scientific practice.
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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 | 0.148 | 0.115 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.042 | 0.082 |
| Scholarly communication | 0.028 | 0.037 |
| Open science | 0.006 | 0.040 |
| Research integrity | 0.010 | 0.035 |
| Insufficient payload (model declined to judge) | 0.003 | 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".