Vulnerability and policy responses in the face of natural resource discoveries and climate change: introduction
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
Abstract This special issue contributes to the natural resource economics literature by shining a light on the specific challenges and opportunities faced by developing countries that have recently become dependent on natural resources or are particularly exposed to climate change. It is composed of five studies on countries from all regions of the developing world, involving a variety of natural resources and policy issues. Four of the five studies illustrate how computable general equilibrium models are particularly well-suited, despite their relatively limited past use, to the analysis of natural resources. All five studies are led by researchers based in these countries, providing unique insights into the specific local context. The studies underscore the extreme vulnerability that the introduction of significant natural resource revenues and climate change can create in developing countries. They also show how the choice of appropriate policies to avoid the resource curse varies according to country-specific economic conditions.
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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 | 0.001 | 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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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 it