Strategies of Energy Suppliers and Consumer Awareness in Green Energy Optics
Bibliographic record
Abstract
This research aims to identify (1) whether consumers have an impact on the energy sector, (2) to what extent consumers have an impact on the energy sector, and (3) whether there is so-called energy communism. We understand this phenomenon as the process of energy suppliers imposing energy sources on which the end consumer has zero or very little influence. The research, therefore, focused on a B2C analysis, in five selected countries: the USA, Canada, Australia, the UK, and Poland (N = 500). The research subjects are a homogeneous group in terms of the sources of green energy, and the volume of production of this type of energy and its increments. The investigation was conducted using the procedure appropriate for the triangulation of research methods. Three hypotheses were verified. The first one was rejected, which aimed to determine whether individual consumers are guided by green energy in their choices. The second hypothesis—that energy suppliers do not take into account customer needs/expectations and pursue their strategies—was partially confirmed but was also directed for further exploration. The third hypothesis was whether the consumer is free to make the decision to switch energy suppliers—if so, what is the hierarchy of the most and least decisive factors in the choice of supplier? The verification of this hypothesis indicates that there is no specific pattern that consumers follow when choosing an energy supplier.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".