Mobilizing smart grid experiments: Policy mobilities and urban energy governance
Bibliographic record
Abstract
Cities across the US have been looking to urban experiments as a way to demonstrate potential pathways for carbon control, economic development, and resilience. On their own, these experiments are often small in scale and highly localized, embodying a piecemeal approach to urban development and climate governance. In this paper, I examine the relationship between urban experimentation and policy mobilities to understand how these projects have broader significance for climate governance and urban development. Drawing together empirical data from a multisited case study of smart grid experiments in Austin, Texas; Boulder, Colorado; and Chicago, Illinois, I show how governmental rationalities are mobilized, mutated, and transmitted in the processes of urban learning, extrospection, and consultation. While the imperative of cities to respond to climate change is ever more central to urban politics and governance, I find that the logics of experimentation are tied to specific governmental rationalities and norms of conduct that embed limited notions of citizen involvement and engagement in policy. The paper outlines how three elements of an Austin smart grid model—users as test-bed, test-bed as platform, and test-bed as epistemology—reinforce these logics and rationalities. The implications for urban climate and energy governance are outlined stressing three synergies between urban experiments and policy mobilities approaches.
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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.005 | 0.022 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".