Уникальность наукоградов России в контексте системного подхода
Bibliographic record
Abstract
В статье с помощью системного подхода в рамках социально-философского дискурса рассматривается феномен наукоградов в современной Российской Федерации как особый тип организации функционирования научных учреждений в городской среде. Концептуализация наукоградов показана через деятельностный и институциональный подходы. Анализируется сложившаяся в СССР модель организации связи науки и производства и ее трансформация в первой четверти XXI в. В качестве концептуальных моделей функционирования научных институций рассматриваются как исторические формы университета и академии, так и современные варианты технополисов в разных странах мира. Базовыми основаниями концепта выступают открытость и закрытость системы. Подчеркивается, что изменение концепта, в качестве которого выступает научная корпорация, приведет к дискредитации наукограда как уникального урбанистического феномена. Приводятся примеры практик, исключающих подобный сценарий. Using a systematic approach within the framework of socio-philosophical discourse, the article considers a phenomenon of science cities in modern Russia as a special type of organization of the functioning of scientific institutions in an urban environment. Conceptualization of science cities is shown through activity-based and institutional approaches. The article analyzes a model of the organization of communication between science and production in the USSR and its transformation in the first quarter of the XXI century. Both historical forms of the university and the academy, as well as modern variants of research and development cities in different countries of the world, are considered as conceptual models of the functioning of scientific institutions. Basic foundations of the concept are openness and closeness of the system. It is emphasized that changing the scientific corporation concept may cast doubt on science cities as a unique urban phenomenon. Examples of practices that exclude such a scenario are given.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.008 |
| Scholarly communication | 0.011 | 0.007 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.035 | 0.010 |
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".