Inuit, namiipita? Climate Change Research and Policy: Beyond Canada’s Diversity and Equity Problem
Bibliographic record
Abstract
As an Inuk, born and raised in Iqaluit and academically trained in southern Canada, I start my thoughts here with two notable questions that Mary Simon (2017), Minister Bennett’s Special Representative in the cross-sectoral engagement for the new Arctic Policy Framework, kept returning to:"Why, in spite of substantive progress over the past 40 years, including remarkable achievements such as land claims agreements, Constitutional inclusion and precedent-setting court rulings, does the Arctic continue to exhibit among the worst national social indicators for basic wellness?"Why, with all the hard-earned tools of empowerment, do many individuals and families not feel empowered and healthy?"In the same line of inquiry, I ask: Inuit, namiipita? Why, in spite of so much research and policy focus on Arctic climate change, are we Inuit still consultants or fillers in an otherwise Western-driven enterprise to “monitor” climate developments in Inuit Nunangat? This is not to polarize North and South in the otherwise existential task we all have to tackle―climate change. Rather, I want to highlight that the story of climate change research and policy in Canada has so far been the familiar story of marginalization of Inuit in the national narrative; and that it is in Canada’s―indeed humanity’s―interests to have Inuit participate equally and with a sense of utmost urgency in the research and decision-making processes related to the Arctic. It goes beyond the diversity and equity rationale or the moral duty of reconciliation: we simply cannot afford to act differently. ........ continued
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 | 0.012 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.052 | 0.033 |
| Scholarly communication | 0.021 | 0.010 |
| Open science | 0.005 | 0.014 |
| Research integrity | 0.008 | 0.016 |
| Insufficient payload (model declined to judge) | 0.009 | 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".