How Communities can earn a Fair Share of Benefits from Natural Resource Development
Bibliographic record
Abstract
A common way of ensuring that the benefits of resource development are received by impacted communities is through negotiating an impact benefit agreement (IBA). IBAs are contracts between resource developers and local communities that specify the distribution of benefits and mitigation of adverse impacts. But while IBAs are growing in popularity, it is unclear whether the IBAs that are being negotiated are optimal and result in the impacted communities maximizing their incomes from resource development. Our study presents guidelines for how communities should negotiate revenue provisions to ensure an equitable distribution of benefits. We present alternative IBA revenue-generating tools (or fiscal instruments) and their respective advantages and disadvantages. A financial model is presented that can be used to evaluate alternative IBA designs and estimate the income that can be expected by a community. We present recommendations and strategies for choosing the optimal combination of fiscal instruments (a fiscal regime) for the community to achieve a fair share of development benefits.
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How this classification was reachedexpand
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".