Travel Routes, Harvesting and Climate Change in Ulukhaktok, Canada
Bibliographic record
Abstract
This paper presents research that integrates natural and social science data with the knowledge from community members to document the implications of climate change for travel routes, used by community members in Ulukhaktok to access seasonal harvesting grounds, and how policy decisions can enhance capacity to adapt in the future. It outlines steps for engaging arctic communities in climate change research and describes an approach to assessing vulnerability. The approach is applied in a case study for the community of Ulukhaktok, Northwest Territories (NT), Canada. Information was collected from a triangulated set of resources including, community reports, climate records, existing research, and 62 in-depth interviews with community members. Data indicates that climate change together with societal changes have resulted in compromised trail routes to harvesting grounds and increased hazards for travelers. Current adaptive strategies involve traveling via alternative modes of transportation and travel routes, taking extra precautions before and during travel and sharing country foods. Adaptations are not universal among community members and changing trail conditions have resulted in community members spending less time traveling on the land harvesting country foods which has implications for food security, local economy, cultural preservation and health.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.008 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".