Impact and Benefit Agreements: Are they working?
Bibliographic record
Abstract
The emergence of Impact and Benefit Agreements (IBAs) in the Canadian mining sector has been read by many as a positive governance innovation. Negotiated directly between mineral resource developers and Aboriginal communities with limited government interference, IBAs serve to manage impacts associated with a mining project and deliver tangible benefits to local communities. Notwithstanding their increasing use and significance, limited systematic analysis has been undertaken to determine whether they are, in fact, working. This paper reports on the effectiveness of a number of IBAs negotiated in support of three northern Canadian diamond mines, drawing on evidence from time-series data, key informant interviews, and focus group meetings in Yellowknife and Dettah, NWT, and Kugluktuk, NU. While some deficiencies were apparent and perceptions of effectiveness varied somewhat by Aboriginal community, the IBAs were generally found to be meeting their objectives, especially with respect to the delivery of benefits. For Aboriginal communities affected by mineral development in the Canadian North, this represents a significant change to typical outcomes of the past. Moving forward, research on IBA effectiveness needs to adopt a longer timeframe and begin to gauge the degree to which IBAs are able to address long-standing concerns associated with hinterland resource extraction beyond their agreement-specific objectives.
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 | 0.092 | 0.151 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.008 |
| Science and technology studies | 0.014 | 0.042 |
| Scholarly communication | 0.026 | 0.032 |
| Open science | 0.004 | 0.013 |
| Research integrity | 0.006 | 0.007 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".