Bibliographic record
Abstract
The oil and gas industry involves colossal capital investment. It is capital intensive, volatile and replete with risks. Risk allocation as the term implies, is the mechanism of risk apportionment among the key players in the oil and gas industry. The paper tried to evaluate how risks are shared under the various types of oil and gas agreements such as Joint Venture Agreement (JVA), Productions Sharing Contract (PSC) and Risk Sharing Contract (RSC) between the National Oil Company (NNPC) for Nigeria and the International Oil Company (IOC) like Shell BP. This paper also appraised how the liability of the parties to an oil and gas contract can be limited through the insertion of certain clauses into the contract between NOCs and IOCs. Consequently, the paper adopted a doctrinal research method to consider the various documentary evidences available and did a comparative analysis of the practice of risk allocation in the oil and gas industry in Nigeria and other jurisdictions such as United Kingdom, United States of America, Australia and Canada. The paper concluded that risks allocation between NOCs and IOCs is absolutely necessary in view of the humongous capital involved in oil and gas exploration, exploitation and production contracts. The paper recommended that risk allocation in oil and gas contracts between NOCs and IOCs should be taken serious because it determines return on investments, the GDP, revenue and foreign reserves of a country.
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 | 0.007 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.007 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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".