Inuit Harvest Data in Qikiqtait Protected Area Development
Bibliographic record
Abstract
Inuit-led conservation initiatives are being increasingly recognized for their ability to engage community members, support the harvesting of country food, support longterm environmental monitoring, and promote Inuit self-determination. My MA research was conducted in partnership with the Arctic Eider Society and the Sanikiluaq Qikiqtait Steering Committee. The goal was to support the development of the Inuit-led Qikiqtait Protected Area (Qikiqtait) around the Belcher Islands Archipelago, Nunavut, using harvester data collected on SIKU: The Indigenous Knowledge Social Network. Sanikiluarmiut (people of Sanikiluaq) harvest data for 14 key species collected from April 1, 2020 – March 31, 2022, was used to address the following research objectives: i) contribute to the Qikiqtait harvest resource inventory using Inuit harvester data collected on SIKU; ii) compare the harvest resource inventory data to Qikiqtait management priorities; and, iii) explore the capacity of SIKU as a tool to contribute to a community environmental monitoring approach to Inuit-led protected area development and ongoing management. A temporal and spatial analysis was conducted to show harvest density patterns and changes over time. These results showed a change in harvest timing and location for most species over the analysis period and identified the seasonality of Sanikiluarmiut harvesting. This harvest resource inventory creates baseline data for key species that can be used to identify and assess harvesting trends over time. The results of a comparative spatial analysis revealed that the harvest data could complement previously identified Qikiqtait priority areas. The results of this research showed that SIKU is an effective tool to use in Qikiqtait development and can support long-term wildlife monitoring. Recommendations are made to further increase the capacity of the app to address community priorities. This research contributes to the body of work supporting long-term Inuit-led environmental monitoring to promote Inuit decision-making.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".