Exceptions to Copyright for Scientific and Educational Purposes
Bibliographic record
Abstract
In this paper, it was tried to consider the current state of normative support for the development of strategic planning documents in the field of Russian education. The strategy for the development of education (DoE), as an industry document of strategic planning, has not been adopted in the Russian Federation (RF) to date. The article attempts to generalize the developments in this area, considers several international documents and projects of strategies for the development of Russian education (DRE), national strategies focused on them. The analysis of regional strategy for the DoE, which is either component of strategies for socioeconomic development of the region, or separate concepts that are focused on municipal development strategies. Due to the lack of a strategic document at the Federal level, regional development concepts are mostly aimed at solving regional problems in the field of education, at a process approach, and, in fact, are not focused on solving breakthrough problems and taking into account all-Russian and global challenges. It is concluded that the Russian educational system cannot develop effectively in the course of planning only national projects that have their own time horizon; to solve this problem, a strategy for the DRE space is necessary, which can provide guidelines for the development of each educational organization.
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 | 0.013 | 0.074 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.006 | 0.007 |
| Scholarly communication | 0.015 | 0.009 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.012 | 0.012 |
| Insufficient payload (model declined to judge) | 0.286 | 0.418 |
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".