Governing Shale Gas: Development, Citizen Participation and Decision Making in the US, Canada, Australia and Europe
Bibliographic record
Abstract
Shale energy development is an issue of global importance. The number of reserves globally, and their potential economic return, have increased dramatically in the past decade. Questions abound, however, about the appropriate governance systems to manage the risks of unconventional oil and gas development and the ability for citizens to engage and participate in decisions regarding these systems. Stakeholder participation is essential for the social and political legitimacy of energy extraction and production, what the industry calls a 'social license' to operate. This book attempts to bring together critical themes inherent in the energy governance literature and illustrate them through cases in multiple countries, including the US, the UK, Canada, South Africa, Germany and Poland. These themes include how multiple actors and institutions – industry, governments and regulatory bodies at all scales, communities, opposition movements, and individual landowners – have roles in developing, contesting, monitoring, and enforcing practices and regulations within unconventional oil and gas development. Overall, the book proposes a systemic, participatory, community-led approach required to achieve a form of legitimacy that allows communities to derive social priorities by a process of community visioning. This book will be of great relevance to scholars and policy-makers with an interest in shale gas development, and energy policy and governance.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.012 | 0.013 |
| Scholarly communication | 0.011 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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.
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".