Reconciliation and Third-Party Interests: Tsilhqot’in Nation v. British Columbia
Bibliographic record
Abstract
The manner in which conflicts between Aboriginal title to land and private third-party interests should be dealt with is a major issue in Canadian law and policy. The matter came up at trial in Tsilhqot'in Nation v. British Columbia, and again was left unresolved. However, Justice Vickers did acknowledge the vital importance of the issue and the need to reconcile these conflicting interests through honourable negotiations. While admitting that a courtroom is not the appropriate forum for achieving reconciliation, he provided detailed analysis of the applicable legal principles and insights into the public policy considerations that should guide the negotiations. This article examines these aspects of Justice Vickers' judgment and suggests more specific ways in which Aboriginal title and third-party interests might be reconciled through the process of negotiation. It proposes a context-based approach that seeks to redress the historical injustice of the wrongful taking of Aboriginal lands, without disregarding the current interests of innocent third parties. The monetary costs of reconciliation, it is argued, should be borne by the real wrongdoers, namely the provincial and Canadian governments.
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 | 0.013 | 0.030 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.025 | 0.008 |
| Scholarly communication | 0.009 | 0.005 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.009 | 0.012 |
| Insufficient payload (model declined to judge) | 0.008 | 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".