Gwaabaw: Applying Anishinaabe harvesting protocols to energy governance
Bibliographic record
Abstract
Oil and gas extraction has transformed Anishinaabe society in ways that undermine the consensual, holistic, and egalitarian basis of natural law. To many Indigenous people, framing fossil fuels and other energy sources as “natural resources” does not accurately define energy projects or capture related risks. Some Anishinaabe pipeline opponents have suggested that traditional harvesting protocols—culturally embedded moral precepts that govern the gathering of food and medicinal plants—also be applied to activities that produce energy. This paper explores how this could be done, focusing on tar sands extraction and the Line 3 expansion plan. I begin by discussing Anishinaabe harvesting protocols, identifying four overlapping key concepts: rights, responsibility, relationality, and reciprocity. These principles are then mapped onto Anishinaabe understandings of oil, hydro, wind, and solar energy. The resulting analysis challenges extractivist narratives of energy production, opening possibilities to rethink the relationship between people and energy as well as the values that inform energy decisions .
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.005 |
| 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) | 0.000 | 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".