What have we learned? Navigating the climate change research landscape in Nunavut (2004-2021)
Bibliographic record
Abstract
Climate change in Nunavut is rapidly impacting key wildlife, ice and weather patterns, and Inuit travel on land, water, and ice. This, in turn, affects Inuit livelihoods, culture, health, and well-being. In 2022, the Nunavut Research Institute (NRI) and Government of Nunavut Climate Change Secretariat (CCS) identified the need to understand the diversity of climate change projects that have taken place across the territory over the last two decades (2004-2021). Recognizing that not all climate change research conducted is published in academic literature, an analysis of climate change research in Nunavut was undertaken according to licensed and permitted research (from the NRI, Government of Nunavut Department of Environment, Fisheries and Oceans Canada, Parks Canada), as well as federal climate change funding programs targeted to support northern- and Indigenous-led initiatives (Climate Change Preparedness in the North Program, Indigenous Community-Based Climate Monitoring Program, Climate Change and Health Adaptation Program). CCS priority themes were used to analyze licensed/permitted/funded project summaries, including: Built Infrastructure & Services, Community & Connection; Food Sovereignty; Health, Safety & Wellness; Healthy Environment; Inuit Culture & Heritage; and, Livelihoods & Growth. Key findings highlight that: 1) climate change research has increased in Nunavut since 2004; 2) climate change research is led primarily by Canadian Universities, followed by the Government of Canada, and Nunavut Inuit Organizations; 3) most research projects relate to Healthy Environments, with predominant emphasis on physical/natural sciences; and, 4) Nunavut licensing, permitting, and funding agencies can enhance coordination and collaboration to reduce duplicated effort and streamline review processes.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.026 | 0.032 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.016 | 0.006 |
| Scholarly communication | 0.019 | 0.010 |
| Open science | 0.004 | 0.011 |
| Research integrity | 0.002 | 0.006 |
| Insufficient payload (model declined to judge) | 0.004 | 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".