COVID‐19, nationalism, and the politics of crisis: A scholarly exchange
Bibliographic record
Abstract
In this article, several scholars of nationalism discuss the potential for the COVID-19 pandemic to impact the development of nationalism and world politics. To structure the discussion, the contributors respond to three questions: (1) how should we understand the relationship between nationalism and COVID-19; (2) will COVID-19 fuel ethnic and nationalist conflict; and (3) will COVID-19 reinforce or erode the nation-state in the long run? The contributors formulated their responses to these questions near to the outset of the pandemic, amid intense uncertainty. This made it acutely difficult, if not impossible, to make predictions. Nevertheless, it was felt that a historically and theoretically informed discussion would shed light on the types of political processes that could be triggered by the COVID-19 pandemic. In doing so, the aim is to help orient researchers and policy-makers as they grapple with what has rapidly become the most urgent issue of our times.
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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 | 0.016 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.008 |
| Science and technology studies | 0.015 | 0.038 |
| Scholarly communication | 0.021 | 0.025 |
| Open science | 0.002 | 0.012 |
| Research integrity | 0.014 | 0.015 |
| 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".