Arctic corridors and northern voices project: Methods for community-based participatory mapping for low impact shipping corridors in Arctic Canada
Bibliographic record
Abstract
Documenting Inuit and local knowledge is critical to its consideration within policy discussions around Arctic shipping; especially considering the rapid increase in ship traffic due to reductions in sea ice and climate change. We present our unique community-based research approach which incorporated youth training, participatory mapping, qualitative focus group discussions, and verification exercises to document Inuit communities' perspectives in Arctic Canada about Low Impact Shipping Corridors. These qualitative activities provided appropriate context and understanding around community-created maps, community-identified opportunities, concerns, and recommendations, and the policy relevance and feasibility of recommendations posed. Three activity phases were employed; 1) before engaging in in-community research, 2) during in-community research, and 3) after completing in-community research. Spatial and non-spatial data were analyzed using ArcGIS® and NVivo software, respectively. These methods and observations can inform future research initiatives, particularly transdisciplinary teams, including those involving southern-based (early career) researchers, working in Inuit Nunangat.•Methods presented here ensured that scientific processes and outputs were robust and rigorous and research was conducted in a respectful, reciprocal manner.•Only through the collaborative efforts of a transdisciplinary team could scientific rigour be attained and respect be afforded.•The approach can be easily applied to document community members' perspectives on local priorities.
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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 | 0.016 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.009 | 0.003 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.001 | 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".