Bibliographic record
Abstract
Despite the wide proliferation of bordering processes across places, platforms, and populations, movements for border justice often maintain a materialist and geographically narrow focus. Activists draw public attention to the border’s physical infrastructure, challenging the use of barriers, policing, and incarceration to violently prevent and punish transnational migration. To counter this “border spectacle” enacted by the State (De Genova, 2013), protest against contemporary border regimes may take its own spectacular form, whether as sabotage, blockading and disruption, or as humanitarian interventions. Border resistance may also manifest as artistic interventions, including musical concerts, competing with the State over regimes of representation. In this article I consider what these situated interventions reveal about the nature of borders in an age defined by the State’s paradoxical efforts to both materialize (as in the form of barrier building) and dematerialize (as in the form of data driven surveillance) state borders in defense of an increasingly elusive national sovereignty. To do so I examine a quartet of musical concerts staged at (or across) four national borders – Mexico/US; East/West Germany; North/South Korea; Columbia/Venezuela – to demonstrate how artists, activists, and even governments have attempted a type of performative spectacle which simultaneously stages and challenges sovereignty, undermining the border’s function as a limit and temporarily enacting a world without borders.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.017 |
| Scholarly communication | 0.010 | 0.005 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.007 | 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".