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Record W2774697679 · doi:10.29173/alr1247

The Oil and Gas ROFR: Understanding Current ROFR Issues from the Point of View of the Transactional Lawyer, the Litigator, and In-House Counsel

2017· article· en· W2774697679 on OpenAlexvenueno aff
Donald G. MacDiarmid, Sean J. Korney, Melanie Teetaert, Julie J.M. Taylor, Robert Martz, Randon E. Slaney

Bibliographic record

VenueAlberta Law Review · 2017
Typearticle
Languageen
FieldSocial Sciences
TopicLegal Systems and Judicial Processes
Canadian institutionsnot available
Fundersnot available
KeywordsNegotiationInterpretation (philosophy)Context (archaeology)DutyInsolvencyLawPoint (geometry)Law and economicsPolitical scienceEconomicsBusinessHistoryComputer science

Abstract

fetched live from OpenAlex

Rights of first refusal and other preferential or pre-emptive rights (together, ROFRs, and individually a ROFR) routinely find their way into oil and gas industry agreements. Disputes often arise because of the complex nature and significant economic consequences of ROFRs. In recent years, a number of reported cases, either relating directly to ROFRs or more generally relating to contractual interpretation, have clarified (or at times muddied) the waters surrounding the use, application, and interpretation of ROFRs. However, most ROFR disputes never result in a reported decision because the parties typically negotiate solutions long before trial.The authors consider current trends involving ROFRs in oil and gas agreements, and how they believe the law and legal practice surrounding ROFRs might continue to evolve in the years to come. The authors do not attempt to rehash the fundamentals of the law surrounding ROFRs; instead, they focus on how the courts have dealt with ROFRs in recent cases as well as how corporate lawyers and in-house counsel grapple with ROFRs day-today. The authors utilize the ROFR provisions found in industry standard contracts to analyze outstanding areas of uncertainty as well as what lawyers should contemplate prior to including a ROFR in an agreement. Additionally, the article examines the implications of recent rulings on the duty of good faith that may affect ROFRs. Finally, the article considers selected subjects of topical interest, including ROFRs in the context of busted butterfly transactions, insolvency proceedings, and package deals.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: none
Teacher disagreement score0.969
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0020.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.049
GPT teacher head0.330
Teacher spread0.281 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designNot applicable
Domainnot available
GenreReview

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".

Quick stats

Citations0
Published2017
Admission routes1
Has abstractyes

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