Latest developments in oil & gas joint operating agreements and alternative liability allocations between operators and non-operators
Bibliographic record
Abstract
Joint operating agreements(JOAs) determine the degree of cooperation or confrontation between operators and non-operators in international petroleum joint ventures. Model JOAs proposed by petroleum standard-setting associations abroad are widely used in the international petroleum industry. The international oil majors all have their model JOAs, with negotiation over operator-liability clauses becoming increasingly diffi cult and with JOAs over the past few years increasingly favoring inclusion of regulatory compliance clauses, environmental protection clauses, and legal enforceability. Petroleum standard-setting associations such as the Association of International Petroleum Negotiators, the American Association of Petroleum Landmen, the Canadian Association of Petroleum Landmen, and Oil and Gas UK, with their respective model JOAs, have differing views on what standards the operator should adopt in the conduct of joint operations, the defi nition of gross negligence and willful misconduct, and exclusion clauses or liability limitation clauses. Chinese companies should familiarize themselves with the model JOAs proposed by petroleum standard-setting associations abroad, pay close attention to ongoing legislative and judicial developments related to JOAs, master the key negotiating points in operator liability clauses and actively seek out favorable JOAs and explore the options available under different JOA clauses.
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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 | 0.009 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.007 | 0.013 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 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".