Connecting understandings of weather and climate: steps towards co-production of knowledge and collaborative environmental management in Inuit Nunangat
Bibliographic record
Abstract
Inuit hunters and meteorologists alike pay close attention to weather and weather changes, with deep understandings. This paper describes a long-time research project based in Kangiqtugaapik (Clyde River), Nunavut, where a research team of Inuit and visiting scientists have combined information and knowledge from a community-based weather station network, on-going interviews and discussions, and extensive travel (both Arctic fieldwork and visits to southern universities) to co-produce knowledge related to human–weather relationships and weather information needs and uses in one Nunavut community. The project uses the concept of “HREVs”, human-relevant environmental variables — complex, synthesis variables that, used in conjunction with a host of social variables, assist in informing safe land travel and activities. This work, including linking Inuit knowledge and environmental modeling, can be expanded to not only understand human–weather relationships more broadly and in other locations but also provide insights into the process of building diverse research teams and knowledge co-production. Inuit angunasuktiit amma silalirijiit tamarmik ujjiqsuttiasuunguvut silamit amma silaup asijjiqpallianingani, tukisiumaniqarjuaqłutik. Una paippaangujuq unikkaarivuq akuniujumi qaujitasaqtaunirmut piliriangujumi Kangiqtugaapik (Clyde River), Nunavummi, qaujisaqtiujuni katinngajuni Inungni amma pularaqtunut qaujisaqtiujunut katirisimajuni uqausiksani amma qaujimaniujumi nunalingni−tunngavilingmi silalirivvingmi tusaumatittiniujumi, apiqsuqtaunginnaqtuni amma uqallangniujuni, amma aullaaqsimarjuaqłutik (tamakkit Ukiuqtaqtumi iniujumi piliriniujumi amma pulararniujunut qallunaat nunanganni silattuqsarvigjuangujunut) saqqitittiqatigiingnirmut qaujimaniujumi pijjutiqaqtumut inungnut−silamut piliriqatigiingniujuni amma silamut uqausiksani pijariaqarniujunut amma aturniujunut atausirmi Nunavummi nunaliujumi. Piliriangujuq atusuunguvuq isumagijauniujumi “HREVs”, inungnut-atuutilingnut avatimut ajjigiinnginniujunut – nalunaqtuni, katinniujuni isumagijauniujuni aaqqiksinirnut piliri−jusiujumi ajjigiinnginniujuni, atuqatiqaqłuni ilagijaujumi inuuqatigiingujunut ajjigiinnginniujunit, ikajuqsuisuunguvuq aaqqiksuinirmi attananngittumi nunami aullaarniujumi amma qanuiliurniujunut. Una piliriniujuq ilaqaqtumi kasuqatiqarnirmi inuit qaujimajanginni amma avatimut uukturautiqarnirmi, angigligiaqtaujunnaqpuq tukisiumanituangunngittumi inungt-silamut piliriqatigiingniujumi tauvunngaujjiniujumi ammalu asinginni iniujunut, kisiani tunisijunnaqpuq tukisirjuarniujuni piliriniujuni sananirmut ajjigiinngiruluujaqtuni qaujisaqtiujunut katinngajuni amma qaujimanirmut saqqitittiqatigiingniujumi.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.010 | 0.008 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.001 | 0.001 |
| 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".