Getting back to basics: the Victor Diamond Mine environmental assessment scoping proce and the issue of family-based traditional land versus registered traplines
Bibliographic record
Abstract
Proper scoping is essential for any environmental assessment (EA) process. This is particularly true with respect to resource development in the intercultural setting of First Nation homelands of northern Canada. Improper scoping leads to EAs that are flawed for a number of reasons. For example, potentially impacted stakeholders are excluded from the process; thus, the proper collection of baseline information is not possible resulting in inaccurate predictions of impacts and mitigation strategies. We examined whether the approved EA for the Victor Diamond Mine in northern Ontario was properly scoped using criteria identified by the Government of Canada, in their project-specific guidelines developed for the assessment. Our results from the published literature, which included oral history, clearly indicate that the Victor Diamond Mine EA scoping process was based on two erroneous assumptions: that the registered trapline system was the accepted system of land use/occupation in northern Ontario, and that land use/occupancy was based on the treaty-imposed reserve system (not the family-based traditional lands system). Implications for resource development involving indigenous people are discussed.
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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 | 0.035 | 0.053 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.007 | 0.005 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".