Liberian production sharing contracts: a new model for West Africa?
Bibliographic record
Abstract
Liberia has huge potential to be an oil-producing nation in the near future. With an increasing number of hydrocarbon discoveries in West Africa over the past few years and oil discovery in February 2012 in the Narina well offshore Liberia by African Petroleum, interest in Liberia and West Africa should only accelerate. Indeed, major oil and gas companies such as Chevron, Tullow, Repsol, Anadarko and ENI already hold acreage in Liberia, and ExxonMobil recently acquired an interest. This article looks at a new model of Production Sharing Contract, which was developed as part of a recent transaction whereby Canadian Overseas Petroleum (Bermuda) Limited (COPLB) and ExxonMobil Exploration and Production Liberia Limited (ExxonMobil Liberia) acquired 20 per cent and 80 per cent, respectively, of Liberia Offshore Block LB-13 (Block LB-13). This article examines certain key features of the Production Sharing Contract entered into as part of the above transaction and how the issues addressed in this Production Sharing Contract might be applied to other emerging oil nations in West Africa.
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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.005 | 0.011 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.014 | 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 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".