The impact of corona crisis on the economies of major metropolitan areas
Bibliographic record
Abstract
The article presents the results of an operational assessment of the impact of the COVID-19 crisis on the change in the gross urban product (GUP) in 17 metropolitan areas of Russia with a population of more than 1 million people in 2020. The goal of the authors was to try to answer the most actual questions nowadays (early 2021): how deep was the fall of the largest agglomerations economies in Russia and abroad; did the corona crisis have a more negative impact on the largest metropolitan areas then on the rest of the economy? In order to answer these questions, two main tasks were: 1) to assess GUP in 17 largest metropolitan areas of Russia; 2) to consider foreign estimates of the GUP in 2020. For foreign comparisons, the authors use the first published data on changes in GDP and gross urban/regional product in the United States, Canada and Australia. The assessment of GUP in this work is carried out through the assessment of the component of employee compensation and then the transition to the GUP indicator on the assumption that such a ratio of compensation of employees to GDP in a city equals the average of the said ratios for the 17 metropolitan areas. The assessment showed that the real GDP growth rates in 2020 were negative not in all metropolitan areas, and in most of them economic losses turned out to be less than those of the Russian economy as a whole.
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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 | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".