The Nunavut Wildlife Management Board’s Community-Based Monitoring Network: documenting Inuit harvesting experience using modern technology
Bibliographic record
Abstract
Community-based monitoring is a promising strategy for collaboratively documenting knowledge that has become increasingly widespread among Indigenous communities, institutions, and governments across the Arctic. In January 2012, the Nunavut Wildlife Management Board launched the Community-Based Monitoring Network (CBMN) to document current Inuit harvesting practices using modern technology by engaging Inuit harvesters in Nunavut who hunt, fish, gather, and observe wildlife. We provide an overview of the CBMN and discuss the challenges and opportunities of integrating data gathered through the CBMN in co-management decision-making. The CBMN has resulted in the collection of 7225 wildlife harvest and 2623 observation records by 85 harvesters in seven communities during 5594 on the land trips covering a combined area of approximately 400 000 km 2 . The CBMN represents a powerful approach to knowledge production by Inuit harvesters that is relevant to wildlife managers and co-management agencies. However, the data collected through the Nunavut Wildlife Management Board’s CBMN neither follow conventional wildlife study scientific standards, nor match the outputs of participatory Inuit Qaujimajatuqangit social science research. Instead, it represents a hybrid form of the types of information typically used in resource management discussions. Although such data can inform decision-making, further work may be necessary to fulfill this potential. Nunalingni uumajunik nauttiqsuaqarniq aturuminaqtuulluni upalungaijautiuvuq katujjillutik titiraqsivalliajut qaujimanirijaujunik taakkualu atuqtauvalliatuinnaliqtut nunaqaqqaaqsimajut nunalinginnit, pilirivingnut, ammalu gavamaujuni ukiuqtaqtulimaami. Jaannuari 2012-ngutillugu, nunavut uumajulirijirjuat katimajingit (NWMB) saqqittilauqsimajut nunalingni uumajunik nauttiqsuaqarnirmut piliriamik (CBMN) titiraqtauvalliaqullugit ullumiujuq inuit angunasugusingit aturlutik uajamuuqtunik ullumi atuqtauvaliqtunik nunavumi inuit angunasukpaktut, iqalugasukpaktut, nuatsivaktut, amma nauttiqsuaqaqpaktut uumajunik. unikkaaqaqattaqtugut qanuittuuninganik nunalingni uumajunik nauttiqsuaqarnirmut piliriangujumik ammalu uqautaullutik aksururnarningit ammalu piviksaujut nuattiqullugit tusagaksanik qaritaujakkut aturlugu nunalingni uumajunik nauttiqsuaqanirmut piliriangujuq atuqtauqattarniarmata aulatsijiuqatigiingujunut isumaliuqasuaqtillugit. nunalingni uumajunik nauttiqsuaqarnirmut pilirianguningagut nuattisimaliqtut tusagaksanik 7,225-nik aujaujuvinirnut ammalu 2,623-ngujut takujaujut titiraqtaullutik 85-ngujunut angunasuktinut 7-ngujuni nunaliujuni 5,594-ngirsurłutik aullaqtillugit nunami katitainnarillugit nuna aullarviusimajuq anginiqaqtigilluni sikkitaullutik kilaamitus 400,000 km2. Nunalingni uumajunik nauttiqsuaqanirmut piliriangujuq kiggaqtuivuq sanngijumik piliriaqarninginnut qaujimanirijaujut nuatautillugit inungnut angunasuktinut taakkua nuataujut atuutiqarniaqtillugit uumajulirinirmut aulatsijiujunut ammalu aulatsijiuqataujut pilirivinginnut. Kisianili, taakkua qaritaujakkut tusagaksat nuataujut nunavut uumajulirijirjuat katimajingita nunalingni uumajunik nauttiqsuaqarnirmut piliriangitigut maliksisimangittuq piusirijanginnik uumajunik qaujisainirivaktangita qaujisaqtimmariujut maliganginnik, ammalu angummatijaungittut saqqitauvaktut inungnut ilauqataujunut inuit qaujimajatuqanginnut inuusirmik qaujisaqtimmarit qaujisainirivaktangannut. Kisianili, kiggaqtuijut ajjigiingittunik tusagaksanik atuqtaugajuktunik tusaumattiarlutik isumaliuqtiujut isumauliqattaqullugit, tamatumunga iqqanaijakkannituinnariaqaqpugut pijariiqtaujunnaqullugu piviksaliangusimajuq.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.034 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".