Community Development Agreements: The Hardening and Evaluation of a Norm
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it