Enhancing Harvester Safety and Traditional Food Access through Participatory Mapping with the Ka’a’gee Tu First Nation of Kakisa, Northwest Territories
Bibliographic record
Abstract
Northern Canada has struggled with various systemic challenges based on Eurocentric ideologies, policies, and practices. A major challenge Indigenous communities face North of the 60th parallel is their food security and sovereignty. Inuit, First Nation and Métis populations across the North experience 5 to 6 times higher levels of food insecurity compared to the National average (Food Secure Canada, 2020). These communities face concentrated levels of food system issues, which connect to other factors, such as, health and wellness, the supply chain of market foods, governance, a shift away from traditional foods, and the impacts of climate change. Climate change has been altering the ecosystems and landscapes throughout the North and are increasing the risks and challenges harvesters face in accessing traditional foods. This project details a collaboration with the Ka’a’gee Tu First Nation (KTFN) located in Kakisa, Northwest Territories (NWT) where community members describe changes and risks observed on the land due to climate change, as well as adaptation and processes to increase harvester safety. A participatory action research framework, including participatory mapping were used as the project approach. Participatory mapping was used as a tool for data gathering, which supported the transfer of place-based storytelling and traditional knowledge, thus identifying important features that connected with harvester safety. Thematic analysis of the qualitative data was used to structure themes: importance of being on the land, climate change (risks & impact), local adaptation, safety measures and visitor safety. These themes coincide and connect with local harvester safety and well-being. Spatial data was created through the mapping process and added into the preexisting digital community map known as, The Ka’a’gee Tu Atlas. The results provided integral, local information for the community’s use in the hopes of maintaining and improving harvester safety while ensuring access towards traditional food sources.
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 | 0.006 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 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".