Calculating the Incalculable: Principles for Compensating Impacts to Aboriginal Title
Bibliographic record
Abstract
There continues to be significant uncertainty over the scope of Aboriginal rights in Canada, which results in significant uncertainty for infrastructure development in the energy sector. Developing a framework for determining fair and reasonable compensation for potential impacts to Aboriginal title is therefore a pressing need for governments as well as proponents. This article explores options on how to ensure greater certainty in the process of determining appropriate compensation for impacts to Aboriginal title. It conducts an analysis of the nature of Aboriginal title, the present compensation methodology for all land types, and the Australian experience with these matters. The article is intended to consider compensation for impacts to Aboriginal title, although it is recognized that impacts to Aboriginal title are not the sole challenges arising from energy infrastructure development in Canada. Also, the proposed framework does not suggest that all infringements to Aboriginal title can be justified with appropriate compensation and there may be situations where no level of payment can compensate for the impact to the community’s way of life. The article concludes there are at least three potential approaches to determine appropriate compensation for impacts to Aboriginal title, and regardless of the method chosen all will require extensive reform from the present approach.
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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 | 0.044 | 0.068 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.010 | 0.032 |
| Scholarly communication | 0.016 | 0.008 |
| Open science | 0.009 | 0.007 |
| Research integrity | 0.010 | 0.013 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".