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
Introduction. The article studies patent activity in the regions of Russia. The relevance of the research in this area is determined by the importance of the innovation component in economic growth, as well as by the established targets in the Concept of Technological Development of the Russian Federation for the period up to 2030. The aim of the study is to identify possible types and directions of patent policy for different groups of Russian regions on the basis of patent activity factors.Materials and Methods. The empirical material for the analysis includes data from the World Intellectual Property Organization (WIPO) and the Federal State Statistics Service (FSSS) for 2012‒2021. We use linear regression to identify the key factors affecting the patent activity of the regions. The method of hierarchical clustering allowed us to identify groups of regions according to their patent activity.Results. The linear regression showed the statistically significant dependence of regional patent activity on I-activity level of organizations, the number of active fixed broadband Internet subscribers per 100 population and the average of internal costs for research and development per 1 organization in the region. The hierarchical clustering distinguished 5 clusters of regions: “The Leader”, “Innovation centers”, “Regions of high manufacturability”, “Old RD regions” and “Regions-outsiders”. The authors also formulate definitions of the regional patent policy and the national patent policy and present typologies of state patent policy.Discussions and Conclusions. Based on empirical and theoretical analysis, recommendations on further directions for the development of active patent policies were given to groups of regions. The results of the study can be applied in the development and implementation of scientific and technological regional development strategies, and will also be useful to specialists and government officials involved in regulating patent activity in the regions.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| 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 | 0.001 |
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 it