Technological Capacity Building with Drones in Support of Indigenous Natural Resource Governance, Land Rights, and Title
Bibliographic record
Abstract
Resource governance in Indigenous communities is impacted by the dichotomy between Western power and Indigenous knowledge. In this thesis, I use counter-mapping as a methodology to highlight the value of Indigenous knowledge, drawing on geovisualization methods to build Wet'suwet'en technological capacity to expose and contest settler geographies. I will discuss my experience as an unmanned aircraft systems (UAS) trainer with a Wet'suwet'en field technician team and how my insight can impact future trainers' ability to understand the gap in technological capacity and literacy between provincial government agencies and First Nations. The Canadian province of British Columbia (BC) comprises nearly nine hundred and forty-five thousand square kilometers, 95% of which remains subject to Aboriginal title claims. Historically, the government in BC did not sign formal treaty agreements with the vast majority of the Indigenous nations, whose ancestral land title remains unceded. Over a century has passed since government officials first sought to remove the Indigenous nation of the Wet'suwet'en from their territories onto tiny government-allocated reserves and nearly 50 years since negotiations began for a contemporary treaty. However, there are no agreements that cede authority to the Canadian government over Wet'suwet'en territory. Across this time, the Wet'suwet'en have continued their hereditary governance system. This thesis speaks to the current political geographies of First Nations communities in a time where extractive industries threaten reciprocal land relationships that are the basis for Wet'suwet'en hereditary governance system. Based on research with the Wet'suwet'en, this thesis emphasizes three necessary elements for successful training endeavors to build First Nations technical capacity: (1) centralizing of Wet'suwet'en community values, (2) focusing on increasing sustainable capacity, and (3) prioritizing balanced knowledge transference between First Nations and researchers. Interviews found tensions between the silos of provincial government and the basket of hereditary governance systems. Ethnography training found that reinterpretation should be considered as an additional principle of community-based participatory research partnerships using geospatial methods. Moreover, GIS analysis of the data collected through training activities indicates that logging activity on sampled Wet'suwet'en territories has not historically adhered to established provincial environmental protections regulations, specifically in terms of encroachment into minimum riparian buffer zones along the Zymoetz River.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".