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Mending the Fractures: Creating a Multi-Stakeholder Framework for Building Shared Purpose in Unconventional Oil and Gas

2018· article· en· W2907014644 on OpenAlexaboutno aff
Jamie Jones, Peter Bryant

Bibliographic record

VenueKellogg School of Management Cases · 2018
Typearticle
Languageen
FieldEnergy
TopicOil, Gas, and Environmental Issues
Canadian institutionsnot available
Fundersnot available
KeywordsProfitability indexGridlockStakeholderGovernment (linguistics)Face (sociological concept)BusinessPoliticsLocal communityPublic relationsLawFinanceSociologyPolitical science

Abstract

fetched live from OpenAlex

In the summer of 2014, a large energy company was poised to begin expanding its unconventional natural gas operations in northeastern British Columbia in the hopes of capitalizing on the Canadian province's determination to build a liquid natural gas industry. The company had secured mineral rights from the province but had not simultaneously pursued surface rights from a First Nation community that historically had used the land. When a seismic exploration team appeared on the tribe's traditional territory without consulting it, as was customary (and in some cases legally required), the company unwittingly ignited a firestorm of protest from both First Nation and non First Nation local citizens. Recognizing the importance of social acceptance both to operations and profitability, the company sent senior vice president Maria Paquet to participate in fireside discussions with tribal, regional government, and environmental leaders in the hopes of finding some common ground. Could these leaders arrive at sufficient trust and agreement to allow the company to move forward with its plans? Or would the company face gridlock, community blocking, or even financial peril? In a small-group role-playing exercise, students will step into the shoes of each of these stakeholders as they try to forge a path forward that is acceptable to all.

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)0.000
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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.489
Threshold uncertainty score0.780

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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)0.0010.000

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 head0.052
GPT teacher head0.309
Teacher spread0.257 · 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 designObservational
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

Citations0
Published2018
Admission routes1
Has abstractyes

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