Has the Sovereign Wealth Fund of Azerbaijan (SOFAZ) Been Able to Promote Economic Diversification?
Bibliographic record
Abstract
Oil-rich countries have oftentimes been confronted with the challenge of diversifying their economies away from oil dependence given the exhaustible nature of these fossil fuels. Investing in sovereign wealth funds has been one of the most ubiquitous ways of preparing for the post-oil period. Investing in sovereign wealth funds rather than directly injecting the oil revenues in the economy not only precludes the outbreak of the Dutch Disease (which is known for giving rise to an exchange rate appreciation, crowding out non-oil industries and keeping the economy reliant on oil), but it also saves for future generations. Yet, in the case of Azerbaijan, the Sovereign Wealth Fund of Azerbaijan (SOFAZ), founded in 1999, has only increased this reliance on oil. Using the rentier states theoretical framework, this paper will argue that the direct control over SOFAZ exercised by the president and the lack of consultation with the NGOs have made corruption easier, making the task of economic diversification more difficult. This has been possible because through corruption the president has often resorted to oil money to buy peace rather than invest it in economic diversification. As a result, since the foundation of SOFAZ, the country is more reliant, not less, on oil.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".