Exploring energy transition narratives through mayoral insights using artificial intelligence
Bibliographic record
Abstract
This paper explores energy transition dynamics in three Arctic cities: Luleå (Sweden), Fairbanks (Alaska), and Yellowknife (Canada), with a focus on sustainable urban development. Semi-structured interviews with the mayors of these cities provide insights into their decision-making processes and strategies regarding energy transitions. Using Natural Language Processing (NLP) for semantic analysis, the study uncovers implicit priorities, challenges, and aspirations from the qualitative data. The analysis is guided by the theory of planned behavior, which helps to explain the underlying motivations, attitudes, and perceived behavioral control that influence policy decisions. Results reveal common themes such as balancing environmental goals with economic and social concerns, while also highlighting context-specific challenges in each city. This research contributes to the understanding the role of municipal leadership in energy transitions and demonstrates the effectiveness of NLP techniques in extracting meaningful insights from interviews. The findings aim to inform policymakers and urban planners on fostering sustainable energy transitions in Arctic regions.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.010 |
| Science and technology studies | 0.008 | 0.007 |
| Scholarly communication | 0.002 | 0.005 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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; both teacher heads agree on what is shown here.
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".