Changing the role of non-Indigenous research partners in practice to support Inuit self-determination in research
Bibliographic record
Abstract
Efforts to date have not advanced Indigenous participation, capacity building and knowledge in Arctic environmental science in Canada because Arctic environmental science has yet to acknowledge, or truly practice decolonizing research. The expanding literature on decolonizing and Indigenous research provides guidance towards these alternative research approaches, but less has been written about how you do this in practice and the potential role for non-Indigenous research partners in supporting Inuit self-determination in research. This paper describes the decolonizing methodology of a non-Indigenous researcher partner and presents a co-developed approach, called the Sikumiut model, for Inuit and non-Indigenous researchers interested in supporting Inuit self-determination. In this model the roles of Inuit and non-Indigenous research partners were redefined, with Inuit governing the research and non-Indigenous research partners training and mentoring Inuit youth to conduct the research themselves. The Sikumiut model shows how having Inuit in decision-making positions ensured Inuit data ownership, accessibility, and control over how their Inuit Qaujimajatuqangit is documented, communicated, and respected for its own scientific merit. It examines the benefits and potential to build on the existing research capacity of Inuit youth and describes the guidance and lessons learned from a non-Indigenous researcher in supporting Inuit self-determination in research. Pinasuktaujut maannamut pivaallirtittisimangimmata nunaqarqaarsimajunik ilautitauninginnik, pijunnarsivallianirmik ammalu qaujimajaujunik ukiurtartumi avatilirinikkut kiklisiniarnikkut kanata pijjutigillugu ukiurtartumi avatilirinikkut kiklisiniarnikkut ilisarsisimangimmata, uvaluunniit piliringimmata issaktausimangittunik silataanit qaujisarnirmut. Uqalimaagait issaktausimangittunit silataanit ammalu nunaqarqaarsimajut qaujisarningit piviqartittikmata tukimuagutaujunnarlutik asiagut qaujisarnikkut, kisiani titirartauqattanginnirsaukmat qanuq pilirigajarmangaata ammalu ilautitauningit nunaqarqaarsimangittut qaujisarnirmut ikajurtuilutik Inuit nangminiq qaujisaqattarnirmut. Taanna titirarsimajuq uqausiqartuq issaktausimangillutik iliqusiujumik nunaqarqaarsimangittut qaujisartiujut ammalu saqittillutik ikajurtigiiklutik pigiartittinirmik, taijaujuq sikumiut aturtanga, inungnut ammalu nunaqarqaarsimangittunut qaujisartinut pijumajunut ikajurtuilutik Inuit nangminiq qaujisarnirmut. Tavani aturtaujumi piliriaksangit Inuit ammalu nunaqarqaarsimangittut qaujisartiujut tukisinarsititaullutik, Inuit aulattillutik qaujisarnirmik ammalu nunaqarqaarsimangittut qausartit ilinniartittillutik ammalu pilimmaksaillutik makkuktunik inungnik nangminiq qaujisarunnarniarmata. Sikumiunut aturtaujuq takuksaujuq qanuq Inuit aaqiksuijiullutik Inuit pisimajiuniarlutik tinngirartaujunik, takujaujunnarningit ammalu aulatauningit qanuq inuit qaujimajatuqangit titirartaukmangaata, tusaumajjutaukmangaata ammaluikpigijaulutik kiklisiniarnikkut atuutiqarninginnik. Takunangniujuq pivaalliutaujunnartunik ammalu pirurpalliagajartunik maanna qaujisarniujumik pijunnarsiqullugit makkuktut Inuit ammalu uqausiulluni tukimuagutaujunnartut ammalu ilitausimajut nunaqarqaarsimangittunit qausartinit ikajurtuilutik inuit nangminiq qaujisarnirmut.
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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 | 0.114 | 0.092 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.025 | 0.025 |
| Scholarly communication | 0.014 | 0.018 |
| Open science | 0.006 | 0.034 |
| Research integrity | 0.004 | 0.008 |
| Insufficient payload (model declined to judge) | 0.007 | 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".