Bibliographic record
Abstract
Countries rich in oil and gas often derive great wealth from these resources. Yet such countries are also often host to chronic economic problems, regional infighting and democratic deficits—factors which lead to high levels of corruption and lack of government accountability in the oil and gas industry. When neither constitutional nor effective legal rules govern the extraction of oil and gas, the regulation of the industry or the system for disbursing revenues, these problems worsen. One way to reduce the risks is to craft constitutional provisions designed to enhance accountability, minimize disputes and clarify roles and responsibilities. With an eye to the Middle East and North Africa (MENA) region, this report, using comparative examples from around the globe, addresses possible design options for the regulation in constitutions of oil and gas resources. There is, of course, no universal or best approach. The practices of other countries provide valuable lessons; but each country has to decide for itself the best approach to regulating oil and gas resources at a constitutional level, taking into account the political, social and economic context. The topics covered in this report are ownership, management, national oil companies (NOCs) and revenue. Ownership deals with which level of government has title over oil and gas resources; management refers to the processes by which oil and gas are extracted, transported and refined, including who has the authority to grant management rights, and to which parties; NOCs fall under the umbrella of management, as they are state-owned enterprises that may regulate or participate in the production of oil and gas; revenue management details the collection and distribution of oil and gas revenue, as well as the oversight and transparency mechanisms implemented to monitor the flow of revenue.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.074 | 0.023 |
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".