Transparency in Commodity-Rich Countries: Is State Ownership to Blame?
Bibliographic record
Abstract
Since the late 1990s, transparency has emerged as a major governance pillar helping resource-rich countries improve their performance and escape the resource curse. Within this debate, a few scholars have pointed to the correlation between ownership structure and transparency, and have argued that under state ownership, transparency should not be expected, as government officials refrain from strengthening institutions to retain their discretionary power.
 This study attempts to challenge scholarly existing knowledge by comparing transparency performances in two resource-rich countries with similar ownership structures, Norway and Russia. To this end, it analyses data from the Revenue Governance Index (2017). Overall, such a correlation is not confirmed. While in some cases, state ownership can in fact generate greater opacity, the example of Norway confirms that retaining control can also enhance transparency. As a result, it is suggested to look attentively at the features of state ownership, and in particular, at countries’ institutional quality.
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How this classification was reachedexpand
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) | 0.001 | 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) | 0.001 | 0.001 |
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 teacher head, 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".