Co-producing maps as boundary objects: Bridging Labrador Inuit knowledge and oceanographic research
Bibliographic record
Abstract
Climate change is affecting the marine environment in Nunatsiavut, leading to changing sea ice thickness and seasonal timing, and increasing water temperatures. This impacts the lives of Labrador Inuit, whose culture, economy, and history are deeply tied to marine spaces. Recently, research partnerships involving Inuit communities in Nunatsiavut have increased, creating space for Labrador Inuit in large scale marine research agendas. While including Labrador Inuit knowledge is critical for making research relevant to communities, there are challenges to engaging it alongside oceanographic scientific knowledge, as both stem from unique ontologies, at times having different values, scales, and languages of understanding. Boundary work offers a lens to analyze how boundary objects can foster connections between Labrador Inuit knowledge and oceanographic research. This research offers a conceptual exploration of this subject through analysing the co-production of maps representing Labrador Inuit knowledge of ocean features which, as data, were then applied in oceanographic research problems. Framing these maps as boundary objects demonstrates their utility in mobilizing Inuit knowledge into scientific approaches, acknowledging limitations with respect to knowledge that cannot be spatially rendered.
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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 | 0.011 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.010 | 0.017 |
| Scholarly communication | 0.014 | 0.012 |
| Open science | 0.002 | 0.017 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".