Emerging Practices in Community Development Agreements
Bibliographic record
Abstract
Community Development Agreements (CDAs) have the potential to facilitate the delivery of tangible benefits from large-scale investment projects, such as mines or forestry concessions, to affected persons and communities. To be effective, however, CDAs must be adapted to the local context, meaning that no single model agreement or process will be appropriate in every situation. Nonetheless, leading practices are emerging which can be required by governments, voluntarily adopted by companies, and demanded by communities. These practices are grounded in ensuring that all parties are sufficiently informed, capacitated, and prepared to engage in meaningful negotiations regarding how the investor’s operations should benefit local stakeholders. This article reviews existing research on CDAs, as well as available agreements from the extractive sector in Australia, Canada, Laos, Papua New Guinea, Ghana and Greenland. It articulates seven broad leading practices and how different stakeholders could work to achieve more effective agreements.
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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 | 0.050 | 0.053 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.019 | 0.057 |
| Scholarly communication | 0.022 | 0.017 |
| Open science | 0.004 | 0.015 |
| Research integrity | 0.007 | 0.009 |
| Insufficient payload (model declined to judge) | 0.005 | 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".