Including Indigenous Knowledge Systems in Environmental Assessments: Restructuring the Process
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Indigenous peoples around the world are concerned about the long-term impacts of industrial activities and natural resource extraction projects on their traditional territories. Environmental impact studies, environmental risk assessments (EAs), and risk management protocols are offered as tools that can address some of these concerns. However, these tools are not universally required in jurisdictions, and this Forum intervention considers whether these technical tools might be reshaped to integrate Indigenous communities’ interests, with specific attention to traditional knowledge. Challenges include unrealistic timelines to evaluate proposed projects, community capacity, inadequate understanding of Indigenous communities, and ineffective communicatio, all of which contribute to pervasive distrust in EAs by many Indigenous communities. Despite efforts to address these problems, substantive inequities persist in the way that EAs are conducted as infringement continues on constitutionally protected Indigenous rights. This article highlights challenges within the EA process and presents pathways for improving collaboration and outcomes with Indigenous communities.
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.
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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.003 |
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 it