Mending the Fractures: Creating a Multi-Stakeholder Framework for Building Shared Purpose in Unconventional Oil and Gas
Bibliographic record
Abstract
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.
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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 | 0.065 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.041 | 0.071 |
| Scholarly communication | 0.023 | 0.027 |
| Open science | 0.009 | 0.039 |
| Research integrity | 0.012 | 0.012 |
| Insufficient payload (model declined to judge) | 0.007 | 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".