Impact and Benefit Agreements: A Contentious Issue for Environmental and Aboriginal Justice
Bibliographic record
Abstract
Impact and benefit agreements (IBAs) have become a common part of a standard package of agreements negotiated between an industrial proponent and a representative Aboriginal organization. Among other things, IBAs recognize Aboriginal peoples' interests with the land and parallel more broadly with the corporate social responsibility phenomena. IBAs seek to establish a bond based on consultation and support of both parties in a mineral development scenario. Challenges facing IBAs include their confidential nature and their relationship to conventional environmental assessment (EA). IBAs go beyond the regulatory and advisory EA processes and often find themselves in conflict due to overlapping objectives and blurred boundaries. IBAs can perpetuate injustices if benefits are not equally distributed to the community or if monitoring and follow-up on behalf of both parties are not continuous. To consider both challenges and opportunities, brief descriptions and comparison of IBAs and EAs are discussed and questions regarding the advantages of IBAs are considered.
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 | 0.030 | 0.046 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.019 | 0.038 |
| Scholarly communication | 0.025 | 0.025 |
| Open science | 0.005 | 0.012 |
| Research integrity | 0.018 | 0.021 |
| 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".