THE IMPACT OF THE CORONAVIRUS PANDEMIC ON THE FORMS AND STRUCTURE OF EMPLOYMENT
Bibliographic record
Abstract
The coronavirus pandemic almost immediately led to a global narrowing of the global economy, a sharp reduction in aggregate supply and demand. The decline in production was especially felt in the second quarter of 2020, when the recession in most countries of the world had a double-digit value. According to IMF forecasts, in general, the economy in developed countries, even in 2021, will not reach the level of 2019. The global economic downturn is accompanied by a massive reduction in jobs, rising unemployment, especially in industries that are focused on foreign markets and serving foreign consumers (export production, reception and service of foreign tourists, international transportation, etc.). The economic crisis caused by the coronavirus also hit global economic and technological ties, led to their widespread fail in many geographical points of the planet, and increased the risk of fragmentation and regionalization of the global market. All this was adequately reflected in change of global demand for labor, in a significant transformation of the structure, size and location of employment, and the state of the labor market as a whole. At the same time, the global crisis did not equally affect the level of employment in different types of activity, the formation of demand for labor, the labor market as a whole; the structure of jobs and employment in individual professions, instead of a proportional change, was uneven - mainly in an asymmetric form.
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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 | 0.003 |
| 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 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".