Curse or Blessing: natural resources and economic growth - comparison of the development of Botswana, Nigeria, Norway and Canada in early 21st century
Bibliographic record
Abstract
This thesis seeks to verify the concept of so-called resource curse at the beginning of the new millennium. The theoretical part defines the symptoms of the alleged curse, curse transmission channels and criticism of the concept. Compared to other studies dealing with the theme of the resource curse this work is not focused on just one transmission channel. The practical part verifies several hypotheses established by comparing research papers on impacts of natural resources. The validity of the Prebisch-Singer hypothesis, Dutch disease symptoms and a negative impact on political institutions (inclination toward authoritarianism, high level of corruption, high government spending, low efficiency of economic and political decision-making and low investment in education) is verified. For the analysis have been selected two African countries (Nigeria and Botswana) and two advanced countries (Canada and Norway). The last part of this thesis provides policy implications. The results confirm the Prebisch-Singer hypothesis for selected commodities in the long term and some of the symptoms of Dutch disease at the beginning of the new millennium. Hypotheses regarding the impact on the political institutions have not been confirmed, since the results varied across the countries. The high vulnerability of the countries to movements in commodity prices was found.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".