Bibliographic record
Abstract
The pandemic crisis caused by the COVID-19 coronavirus in early 2020 resulted in a series of rapid developments in all areas of political, economic, social and technological life in every country and society, on a global level. The European Commission has announced the Next Generation recovery plan, with a budget of 750 billion euros for the period 2021-2027. The main scope for European Union is to create a greener, more digital and ultimately sustainable Europe, with increased resilience even in future crises. At the same time, energy is seen as vital for the development and prosperity of every country and society, let alone in today's era of interconnection, high technology and globalization. In this study, both secondary research as well as primary qualitative research took place with personal in-depth interviews with experts, such as academics, politicians and enterpreneurs, all of them considered as stakeholders in issues related to the “green” energy. The overall study produces useful conclusions and suggestions that can contribute to a better understanding of green energy and its economic and social impact on society. The new green deal seems to be a first-class opportunity for the radical restructuring of the European economy and the strengthening of institutions through a more dynamic, sustainable, green growth model that is expected to further shield society and the country from a new upcoming crisis which is likely to occur sooner and more ambitiously, according to what is happening in the global environment.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".