Scientific Research in the Field of Children’s Health and Development
Bibliographic record
Abstract
The focus on the interests of children has always been inherent in the Russian State, but the most effective measures have been implemented already in this century: 4 years before WHO announced the Global Strategy for Mothers and Children in 2016, Russian President Vladimir Putin signed the Decree on the National Strategy for Children for 2012–2017, in 2017. The President announced the “Decade of Childhood”, and in 2020 The priority of childhood was enshrined in the Constitution of the Russian Federation. In the Russian Federation, the most child-oriented system of children’s healthcare has been built, in medical and scientific organizations of the pediatric profile of the Russian Federation, specialists with medical, biomedical, psychological, pedagogical, and social education are specially trained to work with children of different ages, and scientific research in the field of pediatrics is carried out by specialized researchers within the framework of special programs focused on the needs of children’s healthcare. A detailed analysis of the structure and content of the projects implemented in 2022 has been carried out. Scientific projects involving children compared to 2018, when the implementation of the “Decade of Childhood” program began, which indicates that scientific research in the field of children’s health and development is voluminous, but uncoordinated. Many topics are carried out simultaneously by several scientific and medical or educational organizations, but they are not coordinated in unified scientific and methodological approaches. At the same time, there are systemic problems, to solve which it is necessary to switch to network scientific research of population health; to strengthen the integration of research teams both in the context of interdepartmental cooperation (the Ministry of Education and Science of the Russian Federation and the Ministry of Health of the Russian Federation — Rospotrebnadzor — Federal Medical and Biological Agency), and joint scientific work with other ministries (the Ministry of Education of the Russian Federation, the Ministry of Sports of the Russian Federation, the Ministry of Culture of the Russian Federation, etc.); as well as to improve financial mechanisms for managing scientific activities.
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 | 0.021 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.008 |
| Science and technology studies | 0.003 | 0.009 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".