Situating corporations in natural resource management: a corporate power map of Treaty 8 territory in northeastern British Columbia
Bibliographic record
Abstract
Globally we are facing numerous ecological crises (Steffen et al., 2015; Turner, 2014). These crises are predicated on and exacerbated by economic, legal, and ethical approaches to the environment that are not equipped to address systemic environmental issues (Brown & Garver, 2009; Pelletier, 2010). These environmental issues result from a system that also colonizes and oppresses Indigenous people (Coburn & Atleo, 2016). Both the social justice crises of colonialism and our collective ecological crises have solutions arising from outside of western capitalist society. This paper builds on the proposition that empowering Indigenous actors can lead to radically different human to human and earth to human relationships (Escobar, 2007; Mignolo, 2007). The author worked with Indigenous actors from Treaty 8 territory in Northeastern British Columbia (BC) to establish research objectives supporting their work. A corporate power map of Treaty 8 territory outlines the environmental impacts of resource extraction and helps to identify, characterize, and challenge the corporate network driving extraction. Corporate power mapping combines GIS, Social Network Analysis (SNA), financial analysis, and qualitative observations to critically assess extractive networks. 33 companies were chosen as the center of this study, including 20 companies driving oil and gas exploitation in BC and 13 companies proposing multi-million-dollar investments in the Treaty 8 region. A SNA of affiliated oil and gas companies provides important reflections on the integration of resource extraction companies across Canada, while ownership networks are used to draw parallels between capital extraction and historical sources of colonial power. In closing some applications of corporate power mapping to challenge resource extraction on Treaty 8 territory are explored, including identification of key companies driving and profiting from extractivism.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.000 |
| 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".