The Covid-19 Pandemic: Territorial, Political and Governance Dimensions of Bordering
Bibliographic record
Abstract
Whether we talk about the global scale of the threat or focus on the disruption and potential reversal of processes and realities that we assumed immutable, the epochal significance of the COVI-19 pandemic is indisputable. Since the 9/11 terrorist attacks, no other event has revived territorial borders and sent territorialist shockwaves across the world as COVID-19. The wave of contagion and fear that shadowed the discovery of the coronavirus at the end of 2019 was followed by a wave of bordering in the spring of 2020 when borders were broadly closed as a kneejerk reaction to a threat perceived largely as external. With a focus on Europe, North America and Africa, this special issue aims to advance our knowledge of the multiscalar and multidimensional dynamics triggered by the COVID-19 pandemic in various border contexts. The articles in this special issue investigate the response of institutional structures and the agency of regional actors in the face of rebordering, the securitización of the pandemic, the essentialization of fear, the disruption of daily life and livelihoods, the contestation of border closures vis-à-vis questions of survival, and the re/deconstruction of borders dictated by shifting power balances supporting contested border regimes. In the aggregate, this collection of articles reminds us that territory and territoriality remain vigorous and fitting instruments in the toolbox of nation states as demonstrated by the rebordering shocks triggered by the coronavirus pandemic across the world.
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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 | 0.003 | 0.011 |
| Scholarly communication | 0.014 | 0.010 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 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".