Russian Drama: Inherited Development Underperformance and Eroding Global Dominance
Bibliographic record
Abstract
The new normal is a conceptual situation where economic and political agents are economically convinced and politically motivated to adapt to temporary austerity in economic growth and political participation. The concept entails a remarkable and rare mix of economics and politics. Focusing on Russia, the paper draws on results from two studies that reflect on underlying weak links in the benchmark economy that support expectations of moderation in economic growth and political participation. One study examines the tendency and causes for the Russian development underperformance (slow growth and sticky distribution when compared to other leading countries). The study makes use of social accounting matrix multipliers. The tendencies are partly due to structural imbalances inherited from the past economy with its state-led shadow agents, and its ethnic regional disparities. The other study looks forward into the future and examines Russian prospects for leadership and influence at the global level. This study makes use of a dominance index composed of the relative sizes of transforming agents (i.e., population) and transformed value (i.e., GDP). Results for Russia suggest that global dominance is eroding, and global marginalization is imminent. Both studies point to difficult choices that Russian leadership have been increasingly facing and continue to face in a drama-like sequence. We briefly comment on likely responses.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.004 |
| Research integrity | 0.000 | 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".