Bibliographic record
Abstract
Angola is an oil-exporting state characterized by great wealth inequality, political instability, and severe underdevelopment despite having one of the highest African Gross Domestic Products (Gross Domestic, 2015). Its condition can be attributed to the resource curse in which rentier states are prone to corrupt regimes and underdevelopment. Rentier states are those in which a significant part of state revenue is taken from taxing natural resource extracting companies (Burnell, 2011, pg. 234). This paper examines existing ideas of the emergence and persistence of the resource curse in order to understand how it may be reversed in Angola. Natural resources alone do not cause the resource curse. There are a number of developed and democratic countries whose economies are largely dependent on resource extraction, like Canada, Norway, and Botswana. Building on the work of Desha Girod (2009), the resource curse is determined to emerge in states with abundant natural resources that have weak institutions at the time resources are discovered. This paper argues that building strong institutions to allocate resource revenues has the potential to reverse the resource curse and that resistors to such change can be overcome if the cost of maintaining authoritarian regimes outweighs the cost of promoting democracy. As Angolan civil unrest grows in response to unequal wealth distribution, more oppression and incentives are required to maintain the current regime. Thus it remains possible that Angolan elites will promote democratic institutions and reverse the resource curse
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.006 | 0.004 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".