FROM “THE GOLDEN VALLEY” TO “THE SILICON TAIGA”: VECTORS OF CULTURAL MEMORY
Bibliographic record
Abstract
A new transdisciplinary direction “memory studies” is actively developing in the world science. The study of the memory phenomenon is conducted in a socio-cultural context. The historiographical analysis shows the need to expand specific research in accordance with the “memorial turn”. The authors reconstruct cultural practices and determine the vectors of cultural memory in Novosibirsk Akademgorodok in the second half of the 20th — first quarter of the 21st century, they considers the role of cultural memory for the synergy and harmony of technological and socio-cultural spheres of society, which determines the novelty of the work. Вy the beginning of the 21st century cultural practices of Early Akademgorodok have formed dynamic socio-cultural complexes: toponymic, memorial-monumental, heritage, intellectual-leisure, attractive, eventful. They are dynamic vectors of cultural memory of the landmark “Novosibirsk Akademgorodok”. The prospects of the research are connected with the social necessity of socio-cultural support for the promotion of the “Akademgorodok 2.0” mega-project with due regard for historical experience and synthesis of images of the nostalgic past and the predicted future — from the “golden valley” to the “silicon taiga”.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.012 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".