Bibliographic record
Abstract
Lockdowns and border closures to manage the ongoing COVID-19 pandemic have caused the greatest global economic shock since the Great Depression. Does this also signal the end of economic globalization, the most significant trend of the past forty years? And if so, what kind of global political economy is emerging from the wreckage? In this article, I argue that COVID-19 is mainly intensifying pre-existing trends, set in motion by the global financial crisis of 2008 and the People’s Republic of China (PRC)’s economic rise. The disruptions to global supply chains wrought by COVID-19 have combined with rising United States–PRC rivalry, growing disaffection with the distributional impacts of global value chains, and automation to catalyze the turn away from globalized production. Meanwhile, amid the economic doom and gloom, financial markets are booming, high on the central banks’ liquidity injections to which they have been addicted since the 2008 crisis. As in the decade since the 2008 crisis, booming markets will likely deepen inequality and resentment, fuelling economic nationalism and eroding support for globalization even more. The governments of relatively small and open economies, such as Australia and Canada, will need to guide their economies more purposefully or find themselves at the mercy of the increasingly confrontational, yet domestically fragile, United States and the PRC.
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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 | 0.002 | 0.004 |
| 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 | 0.010 |
| Scholarly communication | 0.009 | 0.010 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.012 | 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".