<b>Energy literacy: towards a conceptual framework for energy transition</b>
Bibliographic record
Abstract
Energy is fundamental to our existence. And yet, energy remains difficult to understand and discuss, particularly the impacts or limitations of certain energy systems and how energy functions in sociocultural contexts. Bridging theory and practice, energy literacy expands what we know about energy and how we may think about it in the world around us. Acknowledging how the world’s energy supply and use directly connects to the climate emergency, this article demonstrates how energy literacy can offer environmental and sustainability education other ways of addressing the energy transition. Understanding the underpinnings of energy – with integrated aspects of epistemology, ontology, and application (i.e., what energy is, what energy is about, and what energy does) – leads to the question: what approach effectively translates these experiences and knowledges to a wide range of users, learners, and stakeholders? This article proposes a conceptual framework of energy literacy that considers theoretical ideas and concepts to translate complex systems and understand energy more holistically.
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 | 0.007 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.004 | 0.039 |
| Scholarly communication | 0.012 | 0.016 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.006 | 0.006 |
| Insufficient payload (model declined to judge) | 0.005 | 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".