Indigenous peoples in carbon pricing policymaking
Bibliographic record
Abstract
The article contributes new thinking on the exclusion and inclusion of Indigenous Peoples in carbon pricing policymaking. Using a Canadian case to draw broader lessons for other countries and make a conceptual contribution, we ask and answer five questions: (1) who is excluded; (2) why does exclusion happen; (3) how does exclusion happen; (4) what does exclusion cause; and (5) how could policymakers enhance inclusion? To inform and answer these questions, we construct a decolonial theoretical framework and use it to guide qualitative analysis and doctrinal legal analysis of original data, including 34 semi-structured interviews and few court decisions, to enhance thinking on exclusion and how to enhance inclusion in carbon pricing policymaking. The thesis is that Indigenous Peoples are externally and internally excluded because of legal and practical problems in policymaking, and this impacts legitimacy, transparency, justice, policy effectiveness and indigenous reconciliation, and should be mitigated by enhancing transparency measures, prioritizing the value of legitimacy over cost efficiency, and, overall, transformationally rethinking policymaking processes. Altogether, our theory-grounded empirical sociolegal study demonstrates key concepts for thinking about Indigenous inclusion and exclusion, extending the extant public participation literature as applicable to climate, natural resource, and environmental law and governance, and other relevant legal and social science fields. • Indigenous Peoples are excluded from carbon pricing policymaking. • Multiple legal and practical problems in policymaking cause their exclusion. • Exclusion undermines legitimacy, justice, policy effectiveness, transparency and Indigenous reconciliation. • Enhance transparency, prioritize legitimacy over cost efficiency, and rethink the policymaking process to enhance inclusion.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.010 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.030 | 0.036 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".