Nunatsiavut, ‘our beautiful land’: Inuit landscape ethnoecology in Labrador, Canada
Bibliographic record
Abstract
For Inuit in the subarctic transition zone of northeastern Canada, an intimate knowledge of the environment and local biodiversity is crucial for successful traditional activities. This study examines what kinds of landscape features and habitats Inuit of Nunatsiavut recognize and name. During interviews, community members (mostly Elders) were shown photographs from the region and were asked to describe and name salient types of places in Labrador Inuttitut. The most frequently reported geographical units dealt with the region’s topography (e.g., mountain, island, flat-place), hydrology (e.g., river, bay), and superficial characteristics (e.g., bedrock, permanent snow patch). Ecological considerations were also prominent, such as plant associations and animal habitats (e.g., shrubby-place, wetland, caribou-return-to-place). Areas were often characterized by a dominant species or substrate type, being named using the plural form of the species and (or)substrate (e.g., “napâttuk” meaning ‘tree’ and “napâttuit” meaning ‘forest’ or “siugak” meaning ‘sand’ and “siugalak” meaning ‘sandy-area’). Some types of places reported by Inuit were significant mainly for traditional activities (e.g., berry-patch, seal-place, dry-wood-place, danger-place), aiding navigation and resource finding. Integrating Inuit conceptions of ecosystems and their component landscape units with those of contemporary science can improve our understanding of subarctic ecology, benefit climate change adaptation strategies, and Inuit language as well as culture conservation initiatives.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.010 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".