SCIENCE IN LATE SOVIET HIGHER EDUCATION INSTITUTIONS: GOVERNANCE, HIERARCHIES, AND INTERACTIONS
Bibliographic record
Abstract
The introductory article problematizes the understudied issues of the institutional history of science in the late Soviet higher education (HE) from the 1950s to the 1980s, generalizing on the articles of the thematic issue. The authors depart from the idea of multiple modes of governance and the agency of universities and rely on a combination of “history from above” and “history from below”. Analyzing organizational forms along with insti-tutional logics, the authors find that the functioning of HE science was more complex than proponents of its “secondary” character and isolation from the academic and industrial sectors suggest. The increasing substantive and organizational complexity of HE science required more fine-tuned management. However, the development of non-directive regulatory mechanisms and informal interactions, while partially solving this problem, even led to increased tension under the dominance of command-administrative measures. The late Soviet period is charac-terized by the increasing complexity of HEI hierarchy in terms of the volume and nature of scientific research work (R&D). High competition led to the consolidation of a group of leading universities, which later became the forerunners of post-Soviet national research universities. Although HEIs demonstrated remarkable adaptabil-ity to changes in scientific and technological policies, their agentic strategies were reactive, tending to maintain the status quo rather than contribute to institutional changes. The late Soviet period was also marked by the im-plicit spread of pro-market logics and mechanisms of interaction, such as competition, contract research, effi-ciency discourse, economization, and quantification. It enables us to find the deep roots of neoliberal reforms in post-Soviet higher education in Soviet legacy.
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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.005 | 0.012 |
| Scholarly communication | 0.009 | 0.003 |
| Open science | 0.000 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".