Bibliographic record
Abstract
Energy Capitol explores the waning of regulatory politics surrounding large-scale energy systems in the United States at the turn of the millennium. Throughout the twentieth century, large-scale energy systems in North America and Europe were highly regulated by a national political community whose decision-making authority relied on positions of bureaucratic and capitalist-led industry organization. After restructuring in energy markets such as natural gas and electricity during the 1980s, the culture of power surrounding political decision-making began to decline. Against this backdrop, Arthur Mason examines the struggle by oil companies and federal-state agencies to deliver natural gas from Alaska and Canada’s Mackenzie Valley to markets in midcontinental United States, highlighting regulatory collusion to advance their plans. Mason employs perspectives from anthropology, political science, sociology, and science and technology studies to analyze ethnographic data gathered at the Alaska State Legislature and in the Office of the Alaska Governor in Washington, D.C. The focus is primarily on plans for building an estimated $20 billion 3,500 mile pipeline to transport natural gas from the North American Arctic to midcontinental pipeline infrastructure in the United States. By illuminating key aspects of federal-state political decision-making processes on energy transportation infrastructure, Mason highlights the activities of economists, lawyers, and other regulatory intellectuals whose accumulated work impedes Arctic proposals through a reliance on judgments that no longer reflect the conditions in which large-scale projects are increasingly determined. Written by a leading expert in the field, this book will be of great interest to students and scholars of energy policy, environmental politics, governance, and regulation and risk. It will also be relevant to industry professionals working in environmental NGOs and government departments in energy and climate forecasting.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.280 | 0.104 |
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".