Mapping With the Land: Co-developing a Cumulative Impact Monitoring and Land Stewardship Framework with Sambaa K’e First Nation, Northwest Territories, Canada
Bibliographic record
Abstract
Across the Northwest Territories (NWT), Canada, Indigenous populations are striving to achieve effective environmental protection, whilst navigating complex methods, policies, and research relationships within co-management contexts. This thesis seeks to identify how differing cultural systems, environmental change, and fractured partnerships may be unified to align with the needs of the Sambaa K’e First Nation (SKFN), a remote Dehcho Dene community. Indigenous methodologies guided co-development of research questions with SKFN leadership which yielded objectives a) develop a GIS-based method to manage, organize and mobilize cultural and environmental data; b) develop a new stewardship monitoring procedure so that users can apply the former while ‘With The Land” (WTL), and c) test new methods developed in (a) and (b). A mapping rubric developed by the NWT Geologic Survey’s Thermokarst Collective (TKC) working group was expanded to include themes related to biological, cultural, and socio-political change. Interviews, focus groups, and participant observation directed the collection of 195 GPS-link observations which centered Dene perspectives of space and place. This thesis provides SKFN with an improved operational procedure for data collection as well as a functional social framework adapted for the organization of grassroots, community based, intergenerational knowledge exchange. This produced the praxis, ‘Mapping with the Land,’ where youth and Elders are united through cumulative impact monitoring and cultural revitalization, with the assistance of GIS. This aims to increase communication and understanding between local, institutional, and government actors by bridging gaps in research capabilities, local capacity, and recognition of Dene Law.
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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 | 0.019 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.010 |
| Science and technology studies | 0.011 | 0.009 |
| Scholarly communication | 0.013 | 0.004 |
| Open science | 0.004 | 0.008 |
| Research integrity | 0.001 | 0.002 |
| 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".