The Canada - Inuit Nunangat - United Kingdom Arctic research programme
Bibliographic record
Abstract
Arctic and Northern research does not always have a positive legacy with northern communities, from early colonial exploitation through contemporary practices that may not leave positive benefits for communities. In Canada, the Arctic and Northern Policy Framework and the National Inuit Strategy on Research (NISR) provide guidance for how research may enable Inuit-led priorities, and lead to greater Inuit self-determination in research. This paper highlights the principles under which the Canada - Inuit Nunangat - United Kingdom Arctic Research Programme (CINUK) was formed, championing different ways of convening and conducting research in alignment with this guidance. The paper then introduces some of the successful projects funded under CINUK's Mitigations and Adaptations for Resilience thematic area that have relevance to Arctic coasts and oceans; how were these collaborative relationships formed and what are some of the key lessons-learned for developing this type of collaborative research in an engineering context? CINUK endeavors to demonstrate how impactful research can be carried out under a new lens, with Inuit-led research at the centre of Arctic studies.
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 | 0.008 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.011 | 0.003 |
| Scholarly communication | 0.005 | 0.001 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.018 | 0.002 |
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".