A values-centered relational science model: supporting Indigenous rights and reconciliation in research
Bibliographic record
Abstract
Addressing complex social-ecological issues requires all relevant sources of knowledge and data, especially those held by communities who remain close to the land. Centuries of oppression, extractive research practices, and misrepresentation have hindered balanced knowledge exchange with Indigenous communities and inhibited innovation and problem-solving capacity in all scientific fields. A recent shift in the research landscape reflects a growing interest in engaging across diverse communities and ways of knowing. Scientific discussions increasingly highlight the inherent value of Indigenous environmental ethics frameworks and processes as the original roadmaps for sustainable development planning, including their potential in addressing the climate crisis and related social and environmental concerns. Momentum in this shift is also propelled by an increasing body of research evidencing the role of Indigenous land stewardship for maintaining ecological health and biodiversity. However, a key challenge straining this movement lies rooted in colonial residue and ongoing actions that suppress and co-opt Indigenous knowledge systems. Scientists working with incomplete datasets privilege a handful of narratives, conceptual understandings, languages, and historical contexts, while failing to engage thousands of collective bodies of intergenerational, place-based knowledge systems. The current dominant colonial paradigm in scientific research risks continued harmful impacts to Indigenous communities that sustain diverse knowledge systems. Here, we outline how ethical standards in researcher practice can be raised in order to reconcile colonial legacies and ongoing settler colonial practices. We synthesize across Indigenous and community-based research protocols and frameworks, transferring knowledge across disciplines, and ground truthing methods and processes in our own practice, to present a relational science working model for supporting Indigenous rights and reconciliation in research. We maintain that core Indigenous values of integrity, respect, humility, and reciprocity should shape researcher responsibilities and methods applied in order to raise ethical standards and long-term relational accountability regarding Indigenous lands, rights, communities, and our shared futures.
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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 | 0.190 | 0.123 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.016 | 0.166 |
| Scholarly communication | 0.029 | 0.034 |
| Open science | 0.006 | 0.028 |
| Research integrity | 0.009 | 0.011 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".