Dealing with ecological variability and change: Perspectives from the Denesoline and Gwich'in of northern Canada
Bibliographic record
Abstract
Many indigenous peoples have developed knowledge and practices for living with ecological complexity. Indicators and systems of monitoring based on the local and traditional knowledge of the Denesoline and Gwich'in of northern Canada were investigated. Through collaborative case study research we identified indicators of community health, ecosystem health, social-ecological health and ecological variability. Denesoline health indicators are framed around the Dene way of life and the journeys of self-government, healing and cultural preservation. Many different kinds of ecosystem health indicators are used by the Denesoline for understanding and communicating about variability and change in wildlife body condition, wildlife abundance, distribution and diversity, water quality, cultural landscapes and land features were also identified. Gwich'in berry picking activities were the basis for the study of social-ecological health; indicators identified related to individual and family health, social connectivity, cultural continuity, land and resource use, stewardship, self-government and spirituality. The berry picking case study also revealed indicators of ecological variability including species related (e.g. timing/rate of maturation of berries), regional (e.g. temperature), local (e.g. habitats) and site specific indicators (e.g. soil conditions). In addition to indicators, the Denesoline developed a system for monitoring caribou movements using key water crossings known to be bifurcation points to aid them in subsistence harvesting. Monitoring also helps Gwich'in berry pickers make decisions about where, when and with whom to harvest berries. Knowledge generated through monitoring, about variability, appears to be interrelated with the management of this commons resource. Locally developed "rules-in-use" for resource access, sharing information and harvest sharing seem to mirror the relative predictability of the species and also change in response to the abundance and distribution of berries across the region. How can this kind of traditional knowledge be included in resource management decision-making? Legislation and obligations defined in Supreme Court rulings have created clear opportunities in processes such as environmental assessment, however, even where no legal requirements exist, the culture, of co-management created in settled land claim areas seems to have had a spill-over effect into non-settled claim areas. Informal arrangements also appear to increase awareness about the value of Aboriginal participation and traditional knowledge.
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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.043 | 0.017 |
| Scholarly communication | 0.011 | 0.003 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.002 | 0.005 |
| 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".