Challenges And Opportunities For Resource Rich Economies.
Bibliographic record
Abstract
The political economy of resource rich countries is surveyed. The empirical evidence suggests\nthat countries with a large share of primary exports in GNP have bad growth records and high\ninequality, especially if the quality of institutions and the rule of law are bad. The economic\nargument that a resource bonanza induces appreciation of the real exchange rate and a decline\nof non-resource export sectors may have some relevance. More important, a resource boom\nreinforces rent grabbing, especially if institutions are bad, and keeps in place bad policies.\nOptimal resource management may make use of the Hotelling rule and the Hartwick rule.\nHowever, a recent World Bank study suggests that resource rich economies squander their\nnatural resource wealth and more often have negative genuine saving rates. Still, countries\nsuch as Botswana, Canada, Australia and Norway suggest it is possible to escape the resource\ncurse. Some practical suggestions for a better management of natural resources are offered.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.006 | 0.008 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.030 | 0.005 |
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".