Bibliographic record
Abstract
This chapter begins with a discussion of the challenges Russian scholars face today in bringing forward comparative analyses and considers a range of terms which have been proposed for comparative analyses of Russian urbanization. It focuses on territories and regions has directed attention of intranational comparative research away from urban processes. Accounting for the state of a particular methodology in a specific country is always a daunting task. Its overwhelmingly Anglocentric character is reaffirmed by the fact that the authors from the UK, US, and Canada are predominant in journal publishing and the citations. In Soviet regional geography, cities were meant to serve the needs of modernization of the country – to facilitate the development of industry as this was the main historic economic trajectory. Here, the “weak comparisons” of cities in and outside Russia which the author characterized correspond to the arguments developed by Jennifer Robinson about persistence of “a developmentalist characterization of differences among cities”.
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 | 0.012 | 0.040 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.006 | 0.009 |
| Scholarly communication | 0.009 | 0.012 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.037 | 0.005 |
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".