The corona pandemic and its impact on the economic development of the Baltic Sea region in 2020
Bibliographic record
Abstract
This year, the GDP of the ten coastal countries in the Baltic Sea region is set to decrease slightly more than the GDP of the world’s advanced economies on average. The decline in GDP in the Baltic Sea region will be about 7%. The 10% decline in Germany’s GDP in the second quarter is a cause for concern, as Germany accounts for almost 15% of the foreign trade of the other countries in the Baltic Sea region. The fall in oil prices, on the other hand, will hinder the economies of Norway and Russia, while cheaper import energy will provide the other coastal countries in the Baltic Sea region with some relief from the decline in their gross domestic product. The decline in economic growth is already reflected in rising unemployment figures. When increased unemployment is combined with the stress and mental fatigue caused by the pandemic, the risk of increased social unrest grows. Social pressure in the Baltic Sea countries is set to increase the most in Sweden, where a more difficult coronavirus situation than in the rest of the Baltic Sea region, combined with the country’s high youth unemployment rate (almost 30%) and large share of foreign-born population (almost 20%), could result in immigrant-related unrest. Concern has also been raised across the Baltic Sea region regarding the growing number of crimes against persons with foreign backgrounds by extremist movements. Although there is enormous uncertainty concerning the pandemic and its socio-economic impact, it is certain that the world will not return back to what it was even after the corona pandemic has been overcome.
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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 | 0.002 |
| 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 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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 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".