ENERGY CONSULTATIONS ON TREATY 8 LANDS: THE EFFECTS OF OIL EXTRACTION ON PEERLESS TROUT FIRST NATION
Bibliographic record
Abstract
For 14 weeks, from May 8th to August 15th of 2015, I lived and conducted research in Peerless Trout First Nation (PTFN). I use ethnographic research methods and a political ecological framework to study how Indigenous culture and traditional use of the land is affected by resource development in northern Alberta. This research contributes to the discipline of anthropology in several ways. First, it demonstrates how ethnography is an important research tool for collecting scientific data. There are several vignettes, stories, and interview excerpts that, through coding methods, inform the themes and conclusions in this thesis. This thesis is a successful demonstration of graduate work in anthropology and can inform, and hopefully inspire other students to pursue this type of education and work. Second, this thesis contributes to the literature on the anthropology of oil. I review many anthropologists, scholars, and authors who speak to development and the economy; specifically in employment concerns for Indigenous groups living near extraction zones which continues to be a key issue in oil discourse. More generally, this thesis contributes to the larger discipline of environmental anthropologists studying oil and gas in that it supports those who also argue that Indigenous worldview, perspectives, and knowledge must be considered in the making, or perhaps, re-making of consultation, and other laws and policies related to extraction and Indigenous groups. Third, this thesis shares key findings that are useful to PTFN and can inform consultation practice and policy in meaningful ways. My key findings indicate that PTFN remains committed to engaging in industry related projects while highly attuned to the negative social and environmental impacts resource extraction creates. Consultation efforts in PTFN are hampered by several factors including problematic consultation policy and practices, Consultation Staff have limited knowledge of the technical and bureaucratic language used in office and administrative work, and there is limited information sharing between Consultation Staff and inadequate transparency from the Consultation Office to the community about projects. A critical review of these processes is necessary for PTFN to move forward in their efforts to effectively participate in the energy sector in Alberta. I offer tangible suggestions to address these critiques.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".