Bibliographic record
Abstract
The article discusses the status of Global Value Chains (GVCs) amid the COVID 19 pandemic and their influence on world economic development. Key aspects of the world economy and GVCs transformation in the context of the COVID 19 are studied. A brief overview of the economic literature and development of theoretical frameworks and concepts of Global Value Chains as well as globalisation and “slowbalisation” is provided. The article focuses on estimates of key indicators published by international bodies, such as the United Nations, UNCTAD, UNIDO, OECD, WTO, IMF and others. Various think tanks and other institutions such as World Economic Forum, European Central Bank, McKinsey Global Institute, Deloitte, NBER have been analyzing GVCs’ contribution to the transmission of the COVID 19 macroeconomic shocks across countries. A quantitative assessment of participation in GVCs for countries and regions based on available data in the Trade in Value Added (TiVA) database are discussed. Specific attention is paid to the key GVCs indicators, including exports of intermediate goods and foreign value added share of gross exports. Special attention is paid to the economic downturn in the United States and characteristics of GVCs involving enterprises located in Wuhan (China), which is very important to many global supply chains. Various kinds of long-term trends and structural changes are analyzed. It is noted that gross domestic product (GDP) of the USA in constant 2012 prices (ignoring inflation) fell in the second quarter of 2020 compared to the previous quarter by 31.7% but only 9.1% compared to the first quarter of 2020. It is concluded that improving supply chains’ recovery ability will be an important factor for restoring global economic activity in post-coronavirus times.
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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 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".