The Impacts of Climate and Social Changes on Cloudberry (Bakeapple) Picking: a Case Study from Southeastern Labrador
Bibliographic record
Abstract
The traditional subsistence activities of Indigenous communities in Canada’s subarctic are being affected by the impacts of climate change, compounding the effects of social, economic and political changes. Most research has focused on hunting and fishing activities, overlooking berry picking as an important socio-cultural activity and contributor to the diversity of food systems. We examined the vulnerability of cloudberry (referred to as ‘bakeapple’ consistent with local terminology) picking to environmental changes in the community of Cartwright, Labrador using semi-structured interviews ( n = 18), field surveys, and satellite imagery. We identified the components of vulnerability including: the environmental changes affecting the abundance, quality, and ripening time of bakeapples (i.e., exposure), the characteristics of the community that affect how these changes have local impacts (i.e., sensitivity), and the ways in which the community is responding to environmental changes (i.e., adaptive capacity). Our results confirm that environmental changes related to permafrost, vegetation, and water have occurred at the bakeapple picking grounds with observed impacts on bakeapples. It is becoming increasingly difficult for bakeapple pickers to respond to variable growth as in the past because of changes in summer settlement patterns that place families farther from their bakeapple patches. We conclude that harvesters in Cartwright have high adaptive capacity to respond to environmental changes due to their knowledge of their bakeapple patches, and at present, socioeconomic changes have had a greater impact than environmental changes on their harvesting capacity.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.011 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".