Opening the wormhole: Linkages between justice in energy transitions and energy systems modelling literature – implications for policy development
Bibliographic record
Abstract
Abstract Sometimes academics from different disciplines feel like they are working on different planets that orbit stars lightyears apart. Justice in energy transitions and the energy modelling literatures are no exception. While both fields share a common focus on realizing a sustainable and equitable future, rarely do equity and justice considerations enter modelling studies, and vice versa, rarely do papers consider modelling and engineering analysis enter into the justice in energy transitions field. This paper documents collaborative research conducted by justice in energy transitions and energy modelling researchers Through literature reviews and collaborative dialogue, we identified overlaps between justice in energy transitions and energy modelling and set an initial research agenda. By opening the wormhole connecting our fields, we hope to inspire more transdisciplinary research and inform future justice-oriented energy policy. We call on fellow justice in energy transitions and energy modelling researchers to join us in learning from one another and working towards a more sustainable and just future. We also call on policymakers to utilize transdisciplinary research to inform just energy futures.
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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 | 0.045 | 0.118 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.005 | 0.008 |
| Science and technology studies | 0.007 | 0.039 |
| Scholarly communication | 0.025 | 0.038 |
| Open science | 0.003 | 0.014 |
| Research integrity | 0.008 | 0.014 |
| Insufficient payload (model declined to judge) | 0.013 | 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".