Digital Economy in the Context of Phylogenesis of Innovation and Market Development
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Understanding the phylogenetic origin of a concept of innovation stands as the main precipice in establishing a sustainable concept of innovation. And as a scientific direction in studying emergence, distribution and commercialization of innovations. Primary Novelty of present article is expressed through analysis of neoindustrialization as a process of transition to a new economic paradigm through renewal of industrial infrastructure and its form of organization in a Technetronic phase of development. Comparative, comprehensive and factor analysis stands as the main methodology for the present article. Primary data consists of government and commercial statistics. The empirical analysis shows the importance of the vertically integrated structures in the course of new cluster development as well as their weight and importance in the development of the modern digital economy.Results of a research of Economist Intelligence Unit in 82 countries of the world say that such countries as Mexico or China, quickly improve the skills in the field of innovations. The research allowed being elicited one remarkable fact: the countries with the average level of economic welfare have additional benefits that introduction of domestic innovative developments stimulates also faster development of foreign experience.
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.
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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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 it