Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The paper is aimed at assessing scale and trends of urban shrinkage in post-Soviet Russia both at national level and by its major regions. Based on the calculation of average annual index of population loss according to population censuses (1989–2021) data, almost half of Russian cities in total have been shrinking for at least one of three intercensal periods. At the same time, in one of three centers the average annual depopulation exceeded 1% at the end of the entire period. In 1989–2002, the number of shrinking cities was not significant (less than a quarter in total), while increasing dramatically in subsequent inter-census periods to over than 1/3 of all urban settlements of the country by 2021. Study of spatial spreading of urban shrinkage phenomenon unveiled that its progress at different stages was mainly contributed either by resource-based cities of the northern and eastern parts of the country, or by urban settlements in old-developed regions, primarily the Non-Chernozyom areas. Absolute majority of all shrinking cities (87%) are minor units with a population under 50,000 inhabitants. Taking into account the general unfavourability of depopulation and the instability and variability of trends, six types of urban shrinkage trajectories with various combinations and alternations of depopulation phases were identified based on the sequence of depopulation phases within each of the three intercensal periods.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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 it