Bibliographic record
Abstract
The use of rationalized risk assessment to identify the costs and benefits of protecting Aboriginal sacred sites is ubiquitous in Canadian law. Like other contemporary critics of cost-benefit analysis, I voice concerns with its use to adjudicate moral claims and recognize that it can misidentify the depth of loss experienced by Aboriginal peoples when sacred sites are destroyed. Nonetheless, in this article, I question in what ways technocratic approaches to risk could be helpful in protecting sacred sites. The article draws on two recent environmental assessments, the Prosperity Gold-Copper Mine Project in British Columbia and the Screech Lake Uranium Exploration Project in the Northwest Territories, to argue that innovative approaches to characterizing loss illustrate the potential of rationalized methods to identify harm better than it has in the past. The panels’ recommendations to reject the projects, based on the risk that the communities would suffer mental and psychological harm, reflect a genuine effort to provide decision makers with the real cost of approving these two projects. While I do not suggest that cost-benefit analysis can represent the loss of absolute values, I argue that, if done with cultural context in mind, assessment may help to extract the type of information needed to find the depth of empathy from which legal solutions may be constructed.
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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 | 0.007 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.027 |
| Scholarly communication | 0.009 | 0.009 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 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".