Bibliographic record
Abstract
The story of energy is one of profound transition. In little more than a century, the world economy moved from felled forests to coal to oil and gas to synthetic fuels and renewable sources. With these changes came shifts in energy markets, the structure of firms, the scale of risks, and new attempts to coordinate responses to those risks. Most recently, nations and cities alike have begun to adopt green industrial policy. Social movements now demand that, to avoid catastrophic climate change, we leave much of our remaining burnable carbon, including Arctic oil and Canadian tar sands, in the ground. Yet fossil-fueled industry has for some time outpaced not only the assimilative benevolence of atmosphere and ocean, but also our capacity for prediction and control. As legal scholars interpret descriptive elements of Intergovernmental Panel on Climate Change reports and make normative claims about our carbon-intensive economy, they must grapple with sociotechnical systems at different scales, using multiple levels of analysis and a mix of theoretical tools. They need to understand the political economy of these systems, their path dependence, and their susceptibility to disruption as the target of new social movements. Brooklyn Law School’s 2016 David G. Trager Public Policy Symposium, “The Post-Carbon World: Advances in Legal and Social Theory,” brought together scholars uniquely qualified to discuss how such research can, and should, proceed. They approach energy systems from varied disciplines, including sociology, environmental law, science and technology studies, energy law, and public policy. Yet the aim of this issue is not to showcase the range of perspectives that they bring to the green energy transition. We wish to consider how the research questions that legal scholars ask about the imperative to “decarbonize” state, national, and global economies could be advanced through a richer sense of sociotechnical systems, social movements, and institutional change. To ignore these concerns, to – paraphrasing symposium speaker Sheila Jasanoff – uncritically accept models, misread technology as only material, ignore routine practices as repositories of power, and erase history and time as factors that shape development pathways is to invite unintended consequences, widening inequality, or worse. This issue collects papers by symposium speakers Thomas Beamish, Shannon Elizabeth Bell, Eric Biber, John Dernbach, Scott Frickel, Alexandra Klass, Uma Outka, Shobita Parthasarathy, Jim Rossi, David Spence, Amy Stein, and their co-authors.
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 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 | 0.009 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.007 | 0.019 |
| Scholarly communication | 0.009 | 0.019 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.010 | 0.017 |
| Insufficient payload (model declined to judge) | 0.009 | 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".