Sitting in Many Camps—Innovative Approaches and Methods for First Nations‐Led Research Into Indigenous Peacebuilding
Bibliographic record
Abstract
ABSTRACT In 2021, a desktop review was conducted of published references to First Nations peoples' approaches to conflict and its management in Australia (Project Stage One), culminating in a report published in 2024. This article focuses on Project Stage Two, a complex, innovative research undertaking building on the findings of Stage One, and being developed and implemented as a First Nations‐led project. It describes a fully collaborative research approach to co‐designing, with First Nations peoples, multi‐method techniques for collecting, analysing, interpreting and disseminating research information and outcomes on their approaches to conflict and its management. The approach relies on First Nations' protocols, methodologies and knowledges, as well as research principles such as intellectual humility, cultural responsiveness and respect. The project is guided by the principles and protocols relevant to First Nations research as provided by the Australian Institute of Aboriginal and Torres Strait Islander Studies (AIATSIS) and protocols from the University of Newcastle. The Lead Researcher in this project is a First Nations woman, and the Project Team includes non‐First Nations researchers.
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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 | 0.135 | 0.063 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.011 | 0.020 |
| Scholarly communication | 0.015 | 0.013 |
| Open science | 0.005 | 0.014 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.014 | 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".