Climate Change and Energy Futures - Theoretical Frameworks, Epistemological Issues, and Methodological Perspectives
Bibliographic record
Abstract
Critically re-imagining our energy systems is a major task for addressing climate change. Technological change or market signals do not automatically create new energy futures. Rather, political priorities and action shape the ambitions behind energy transitions. In response, this special issue is dedicated to re-thinking energy futures related to climate change, with attention to the social values and norms, hierarchical structures, and social imaginaries underlying decision-making in a carbon-constrained world. Three cross-cutting themes run across this special issue. First, the papers identify new ways of envisioning and creating low-carbon energy futures that engage a range of social actors. Second, the papers highlight the need for analyses that bridge the global and local scales. Third, this issue emphasizes the importance of dialogue across disciplinary perspectives to strengthen the role of environmental social science in decision-making and community responses to climate change and energy futures.
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 | 0.050 | 0.035 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.013 | 0.014 |
| Science and technology studies | 0.009 | 0.084 |
| Scholarly communication | 0.034 | 0.037 |
| Open science | 0.006 | 0.011 |
| Research integrity | 0.013 | 0.019 |
| Insufficient payload (model declined to judge) | 0.006 | 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".