Development on Indigenous Homelands and the Need to Get Back to Basics with Scoping: Is there Still "Unceded" Land in Northern Ontario, Canada, with Respect to Treaty No. 9 and its Adhesions?
Bibliographic record
Abstract
Scoping includes the establishment of unambiguous spatial boundaries for a proposed development project (e.g., a treaty) and is especially important with respect to development on Indigenous homelands. Improper scoping leads to a flawed product, such as a flawed treaty or environmental impact assessment, by excluding stakeholders from the process. A comprehensive literature search was conducted to gather (and collate) printed and online material in relation to Treaty No. 9 and its Adhesions, as well as the Line-AB. We searched academic databases as well as the Library and Archives Canada. The examination of Treaty No. 9 and its Adhesions revealed that there is unceded land in each of four separate scenarios, which are related to the Line-AB and/or emergent land in Northern Ontario, Canada. Lastly, we present lessons learned from our case study. However, since each development initiative and each Indigenous Nation is unique, these suggestions should be taken as a bare minimum or starting point for the scoping process in relation to development projects on Indigenous homelands.
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 | 0.032 | 0.052 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.007 | 0.017 |
| Science and technology studies | 0.013 | 0.020 |
| Scholarly communication | 0.014 | 0.007 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| 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".