Corporate rationales for the use of Impact and Benefit Agreements in Canada's mining sector
Bibliographic record
Abstract
Mining firms in Canada are incorporating social and environmental interests of their stakeholders into their projects with increasing regularity. Such initiatives have included negotiating Impact and Benefit Agreements (IBAs) directly with impacted Aboriginal communities near resource developments in Canada. This thesis empirically investigates mining firms' rationales to take on time- and cost-intensive initiatives and establish IBAs in Canada when there are already regulatory measures that address social and environmental issues. A review of company-issued corporate documents and interviews performed with mining executives for 14 companies operating in Canada found that mining firms increasingly regard IBAs as an intrinsic part of the permitting process. When queried, executives regularly identified their motivation for negotiating agreements as 'the right thing to do'. While seemingly altruistic, this response was, in fact, rooted in a business-case rationale. Firms realize the business value of strong relationships with communities through IBAs, and are accepting their use within the approvals process.
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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 | 0.022 | 0.047 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.027 | 0.011 |
| Scholarly communication | 0.016 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 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".