Schengen Borders as Lines that Continue to Separate? Media Representations of Pandemic Dimensions of Insecurity in Eastern German Border Regions to Poland
Bibliographic record
Abstract
At the internal Schengen borders, integration has long been a guiding paradigm. Nevertheless, it has never been an uncontroversial value. During the COVID-19 pandemic, demands for border closures within the Schengen space were characterized by a new urgency driven by biopolitical attempts to safeguard the respective communities. Our article focuses on the East German border regions to Poland in this conflicting situation. In the light of this crisis, the publicly shared stories and pictures of people living alongside this border demonstrated the strong entanglement of everyday life at this border. By means of an analysis of media articles published during the first two waves of the lockdown, the paper carves out how dimensions of insecurities in the border region arise due to contested negotiations over national orientations and integration. When the borders were shut down in March 2020, media coverage showed strongly rising protest in German–Polish borderlands, from medical staff and care workers, from students and trade people, from artists as well as local administrations. The responses to the COVID-19 pandemic show the potential to fuel debates on resurging nation–state-based politics and to promote nationalist, populist and reactionary positions, seizing upon insecurities and campaigning with emotional politics.
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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 | 0.007 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.000 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".