Managers for sustainable electric power industry of tomorrow
Bibliographic record
Abstract
To be able to lead a large-scale technological overhaul of the electricity generation sector and actively operate in energy markets, it is necessary to substantially increase the professional level of management. Moreover, there is no doubt that the social responsibility of power engineering as a vital infrastructure industry will only continue to increase, competition will intensify while the existing shortage of knowledge will grow. This is happening amid overall instability, uneven and chaotic demand for energy on the part of economic systems. On the other hand, energy consumers, investors and society as a whole are putting an increasing emphasis on reliability, pricing transparency and environmental impacts. The study provides substantiation and a new research and methodology platform for forward-looking education of energy managers. Its purpose is to equip professionals with knowledge and competencies that they will need for working in emerging technological and organisational and economic systems that will be based on new principles and will function in a turbulent external environment. The global energy education is used as an example of building educational content and methodology for the forward-looking training of future energy leaders.
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 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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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; a candidate call from one teacher head, 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".