MétaCan
Menu
Back to cohort
Record W84668195 · doi:10.29173/alr404

Strategies for Risk Management and Corporate Social Responsibility for Oil and Gas Companies in Emerging Markets

2016· article· en· W84668195 on OpenAlexaffvenue
Stephanie Stimpson, Jay Todesco, Amy Maginley

Bibliographic record

VenueAlberta Law Review · 2016
Typearticle
Languageen
FieldEngineering
TopicReservoir Engineering and Simulation Methods
Canadian institutionsNexen (Canada)
Fundersnot available
KeywordsBusinessEmerging marketsRisk managementFossil fuelKey (lock)Resource (disambiguation)Legal riskCorporate social responsibilityValue (mathematics)Risk analysis (engineering)Industrial organizationFinancePublic relations

Abstract

fetched live from OpenAlex

Oil and gas companies are constantly in search of opportunities to expand their resource base and create value. Emerging markets can provide companies with opportunities for significant rewards, especially in regions where oil and gas resources may be underdeveloped and where new technologies have not yet been exploited. However, emerging markets also pose numerous challenges and risks, which can potentially lead to significant legal and reputational damage. This article explores key legal risk areas for oil and gas companies in emerging markets and best practices for managing those risks and operating in a socially responsible way, recognizing that risk management centers around controlled and reasoned decision-making, not eliminating risk. The article is intended to provide a high-level overview of the key legal risk areas and mitigation strategies to serve as a guide for directors and management teams operating in these challenging regions as opposed to providing a comprehensive discussion on any particular risk area.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.836
Threshold uncertainty score0.401

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.035
GPT teacher head0.302
Teacher spread0.268 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreEmpirical

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

Citations1
Published2016
Admission routes2
Has abstractyes

Explore more

Same venueAlberta Law ReviewSame topicReservoir Engineering and Simulation MethodsFrench-language works237,207