Community Development Agreements: The Hardening and Evaluation of a Norm
Bibliographic record
Abstract
Large scale mining projects generate highly variable outcomes. Proponents of mining cite benefits including job creation and revenue generation, while critics point to adverse social and economic impacts borne by mining-proximate communities. Community-based concerns about mining operations have raised ethical and social justice considerations relating to human-rights and consent. Community development agreements (CDAs) have emerged as an increasingly common tool to address such concerns and facilitate the delivery of tangible benefits from mining operations to affected communities. The effectiveness of CDAs, however, varies widely depending on the negotiated provisions and their implementation. This work contributes to the understanding of CDAs by refining a comprehensive evaluation framework that can be used to empirically analyze CDAs. The framework is applied to CDAs from Australia, Canada, Papua New Guinea, Ghana, Greenland, Mongolia, and Sierra Leone, following which exploratory statistical analyses are conducted to highlight novel insights that can be drawn from its application.
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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 | 0.266 | 0.395 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.009 | 0.042 |
| Scholarly communication | 0.017 | 0.018 |
| Open science | 0.003 | 0.015 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.003 | 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".