Spatial analysis of community observations of environmental change in North West River, Labrador
Bibliographic record
Abstract
The Sivunivut Inuit Community Corporation (SICC) is funded by the Nunatsiavut Government to represent North West River (NWR) residents who are beneficiaries of the Labrador Inuit Land Claims Agreement. In 2010, they began work on a community driven, participatory research project that was aimed at addressing community concerns regarding the ongoing effects of climate change on local resource use. This resulted in the completion of forty-nine surveys by resident land use experts. These semi-directed surveys were completed in conjunction with a spatial component and were designed to collect traditional ecological knowledge related to the local effects of climate change. This thesis examines this knowledge using grounded theory and spatial analysis to identify and describe thematic patterns. The spatial extent of environmental change is described through the use of kernel density analysis (KDA) maps illustrating concentrations of environmental change. The regions identified denote commonly referenced areas that, in many cases, are highlighted in multiple survey categories. Through this analysis it is clear that local environmental change is influenced by many factors. Climate change is one such factor but other major drivers of environmental change include weather events, harvesting pressure, infrastructure development, accessibility, and habitat quality. In the face of ongoing environmental change, residents of NWR employ a number of adaptation strategies to maintain their strong connection to the land. This thesis examines aspects of these adaptation efforts in light of a number of technological, societal and political factors.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".