<i>Nuna Aliannaittuk Auttuq</i>—Thawing of the Beautiful Lands of Inuvialuit: Lessons for Sensing Policy
Bibliographic record
Abstract
Abstract This article, co‐led by an Inuk scholar, Inuvialuit graduate student, and non‐Indigenous academic co‐investigators, explores the implications of climate displacement research for public administration, policy, and governance, with a specific emphasis on sensing in relation to policy. Drawing on diverse forms of evidence, we highlight the role of storytelling and sensory experience in shaping climate policy. We reflect on the outcomes of a unique gathering, “Changing Climate Conversations,” where Inuvialuit climate change leaders engaged with Environment Canada officials. Through unipkait (Inuit forms of storytelling) we used murals, music videos, and film, to evoke Inuvialuit youth knowledge on climate change in Tuktoyaktuk, Northwest Territories, Canada. The youth deliberations challenged conventional climate discourse, emphasizing the importance of Indigenous knowledge and perspectives in policymaking. Our findings underscore the need for justice‐oriented policies that honour diverse voices and promote ecological and social justice, enacting inclusive policy futures that center Indigenous sovereignty in environmental governance.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.023 | 0.009 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".