Bibliographic record
Abstract
The COVID-19 pandemic has significantly impacted globalization by disrupting the course of international economic integration, reducing interpersonal interaction and communication, and lessening the significance of global governance and political interactions. This unprecedented event has altered global supply chains, MNEs’ operations and FDI, and trade patterns, and it has favored protectionist and border policies. Meanwhile, travel restrictions and social-distancing measures reduced human mobility and hindered intercultural exchanges. This study explores the short-term and long-term effects of the COVID-19 pandemic on economic globalization while also reflecting on its implications for social and political globalization. The analysis underlines that the COVID-19 pandemic has encouraged many governments to assess their strategies vis-à-vis globalization by seeking a certain equilibrium between global engagement, regional retreat, and national seclusion. Despite the adverse implications, some positive outcomes have emerged via the COVID-19-induced digital transformation and the reconfiguration of the global supply chains to improve resilience against future exogenous shocks. This pandemic exposed the shortcomings of the current global system and emphasized the necessity for a post-COVID-19 “re-designed” globalization to mitigate anti-globalization sentiments and expand benefits across countries/geo-economic regions and different segments of society.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".