Integration of Inuit Land Safety Knowledge into Inuit Nunangat Search and Rescue: A Scoping Review
Bibliographic record
Abstract
The demand for search and rescue (SAR) interventions in Inuit Nunangat has risen considerably over the past decade. Yet, the effectiveness of Inuit Nunangat SAR faces serious challenges: a vast geography with SAR resources located in the South; limited training and supplies for Inuit; lack of integration of Inuit Knowledge into SAR processes; and a rapidly changing climate and environment. Given the increasing demand for SAR services in Inuit Nunangat, using a scoping review methodology, this research identified, analyzed, and reported on the nature, range, and extent of literature on Inuit Nunangat SAR operations in the published literature. Of the 2,728 articles identified, 40 were analyzed. Key findings included: the integration of Inuit land safety knowledge in SAR operations is limited, yet essential; further resources to expand and enhance Inuit Nunangat SAR processes and effectiveness is a priority within a changing climate; and Inuit-led research to enhance SAR is recommended.
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 | 0.042 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.012 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".