Indigenizing Climate Policy in Canada: A Critical Examination of the Pan-Canadian Framework and the ZéN RoadMap
Bibliographic record
Abstract
Climate policies and plans can lead to disproportionate impacts and benefits across different kinds of communities, serving to reinforce, and even exacerbate existing structural inequities and injustices. This is the case in Canada where, we argue, climate policy and planning is reproducing settler-colonial relations, violating Indigenous rights, and systematically excluding Indigenous Peoples from policy making. We conducted a critical policy analysis on two climate plans in Canada: the Pan Canadian Framework on Clean Growth and Climate Change (Pan-Canadian Framework), a federal government-led, top-down plan for reducing emissions; and the Québec ZéN (zero émissions nette, or net-zero emissions) Roadmap, a province-wide, bottom-up energy transition plan developed by civil society and environmental groups in Quebec. Our analysis found that, despite aspirational references to Indigenous Peoples and their inclusion, both the Pan-Canadian Framework and the ZéN Roadmap failed to uphold the right to self-determination and to free, prior, and informed consent, conflicting with commitments to reconciliation and a “Nation-to-Nation” relationship. Recognizing these limitations, we identify six components for an Indigenous-led climate policy agenda. These not including clear calls to action that climate policy must: prioritize the land and emphasize the need to rebalance our relationships with Mother Earth; position Indigenous Nations as Nations with the inherent right to self-determination; prioritize Indigenous knowledge systems; and advance climate-solutions that are interconnected, interdependent, and multi-dimensional. While this supports the emerging literature on Indigenous-led climate solutions, we stress that these calls offer a starting point, but additional work led by Indigenous Peoples and Nations is required to breathe life into a trueIndigenous-led climate policy.
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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 | 0.021 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.012 |
| Science and technology studies | 0.045 | 0.021 |
| Scholarly communication | 0.021 | 0.004 |
| Open science | 0.005 | 0.006 |
| Research integrity | 0.004 | 0.009 |
| 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".