ScIQ: an invitation and recommendations to combine science and Inuit Qaujimajatuqangit for meaningful engagement of Inuit communities in research
Bibliographic record
Abstract
Researchers wishing to conduct studies in Nunavut are asked by potential funders and licensing agencies to incorporate Inuit Qaujimajatuqangit (IQ) and meaningfully engage Inuit communities, but they must usually interpret for themselves what this means and how to do it in practice. As a group of Inuit youth from four Nunavut communities, we have developed a concept we call ScIQ (pronounced sigh-cue) to describe how science and IQ can be combined for more meaningful engagement to benefit both Inuit communities and scientific researchers. ScIQ is based on the understanding that IQ is not only knowledge that Inuit have gained over many generations; it is more holistic and includes Inuit values, customs and principles for living our lives. Incorporating IQ into research then, should be as much about how research is conducted as it is about data collected from Inuit and local knowledge used to conduct the research. Over a five-day Ikaarvik Youth ScIQ Summit in Cambridge Bay, Nunavut, we developed 45 recommendations for specific things researchers can do before, during, and after their research that, from our perspective, are examples of truly incorporating IQ and result in more meaningful engagement of Inuit communities. This paper presents the Ikaarvik ScIQ recommendations. Qaujisaqtiit qaujisarniqarumajut Nunavummi apirijauvut kiinaujaqaqtiutuinnarialingni amma laisansitaaqtittijiujuni ilaliujjinirmut Inuit Qaujimajatuqanginni (IQ) amma tukiqattiaqtumi ilautittinirmi Inungni nunaliujuni, kisiani tukiliurijariaqaqput immingnut qanuq tukiqarningani ammalu qanuq pilirianguvangningani atuqtauninganut. Katinnganiulutik Inungni makkuktuni tisamani Nunavummi nunaliujuni, pivalliatittisimavugut isumagijautuinnarniujumi taijavut ScIQ (taijausuuq sigh-cue) unikkaarinirmi qanuq qaujisarniq amma Inuit Qaujimajatuqangit katitirijaujunnarningani tukiqattiarniqsaujumi ilautittiniujumi pivaallirutiqarniaqtumut tamakkini inungni nunaliujuni amma qaujisarnirmut qaujisaqtiujuni. ScIQ tunngaviqaqpuq tukisiumaniujumi Inuit Qaujimajatuqangit qaujimanituinnaunnginningani Inuit pisimajanginni arraagugasaalungnut, iluittuuniuvuq amma ilaqaqpuq Inuit pinnarijanginni, atuqpaktanginni amma tunngaviujuni inuunirmi inuusittinni. Ilaliujjiniq Inuit Qaujimajatuqanginni qaujisarnirmut asuilaak, ilaqalluaqpuq qanuq qaujisaqtauninga pilirianguvangningani ammalu qaujisaqtaunikuni titiraqsimajuni katiqsuqtaujuni Inungni amma nunalingni qaujimaniujunut atuqtauvaktuni pilirinirmut qaujisarniujumi. Tallimanut−ullunut, Ikaarvik Makkuktuni ScIQ Katimaniujumi Iqaluktuuttiaq, Nunavummi, pivalliatittilauqpugut 45-ni atuliqujaujuni nalunaiqtausimajunut kisutuinnanut qaujisaqtiit pilirijariaqaqtanginni sivuniani, taikani amma kinguniagut qaujisarninginni, isumagijattinni, uuktuutiuvut ilaliujjillaringningani Inuit Qaujimajatuqanginni amma pitittilluni tukiqarniqsaujumi ilautittiniujumi Inungni nunaliujunit. Una paippaaq tunisivuq Ikaarvik ScIQ atuqunajaqtanginni.
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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 | 0.071 | 0.105 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.004 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.015 | 0.010 |
| Scholarly communication | 0.015 | 0.012 |
| Open science | 0.007 | 0.035 |
| Research integrity | 0.024 | 0.026 |
| Insufficient payload (model declined to judge) | 0.059 | 0.036 |
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".