The benefits of Indigenous-led social science: a mindset for Arctic sustainability
Bibliographic record
Abstract
Abstract The Peoples of the Arctic and Arctic health and sustainability are highly interconnected and essentially one and the same. An appropriate path to a sustainable Arctic involves a shift away from individual learning and achieving toward community leadership and the betterment of society. This article draws upon mindset theory from Western psychology and Indigenous relational accountability to propose and outline a model for achieving sustainability in the Arctic. The geographic focus is the North American Arctic. The principles of the argument and the foundations of the model may apply across the Circumpolar North. The paper is a call to action for social scientists and policy makers in the Arctic to implement an Indigenous-led and self-determined social science. Empowering and supporting Indigenous leaders and scholars to direct and conduct autonomous social science research would inherently produce well-being and sustainability for Indigenous communities and regions. The arguments are supported by an inductive analysis of peer-reviewed literature, and the model is organized and illustrated using a schematic of concentric circles. The foundational elements of the model include: Indigenous sovereignty, Indigenous ontology, Indigenous models of sustainability, and Indigenous scholarship. Environmental scientists, resource managers, and policy makers are directed to better understand, accept, and support Indigenous science as a comprehensive and valid knowledge system; change how they use key terminology in research; rethink research roles; and amend processes and timelines for research development and funding. To achieve the desired outcomes for community well-being and Arctic sustainability, Arctic social scientists should seriously consider centering Indigenous science, especially in Indigenous communities.
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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 | 0.042 | 0.028 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.016 | 0.081 |
| Scholarly communication | 0.014 | 0.015 |
| Open science | 0.002 | 0.014 |
| Research integrity | 0.005 | 0.008 |
| Insufficient payload (model declined to judge) | 0.002 | 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".