Aboriginal Impact and Benefit Agreements: Practical Considerations
Bibliographic record
Abstract
Resource development on Aboriginal lands consultation with Aboriginal peoples and subsequently the negotiation of Impact Benefit Agreements (IBAs). Case law in the area is rapidly emerging and standards for industry have increased. Similarly, the application of land claim agreements, agreements in principle and a variety of provincial and federal legislation creates a complex web of considerations for negotiating IBAs. Drawing on industry experience and existing jurisprudence, what follows is an explanation of: why IBAs need to be negotiated by developers: information on key and contentious elements; critical planning considerations; and guidance for navigating this complex and uncertain territory with a view to creating mutually beneficial commercial relationships.
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 | 0.028 | 0.034 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.015 | 0.020 |
| Scholarly communication | 0.018 | 0.009 |
| Open science | 0.005 | 0.006 |
| Research integrity | 0.015 | 0.009 |
| Insufficient payload (model declined to judge) | 0.007 | 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".