Bibliographic record
Abstract
This article theorizes the eco-algorithmic border as a key formation in the convergence of artificial intelligence, environmental governance, and global border regimes. We argue that AI technologies are increasingly deployed to intensify forms of border violence that frame environmental protection as incompatible with human mobility. This alignment enables the consolidation of anti-migrant and climate-sceptical politics under the guise of technological efficiency. The eco-algorithmic border propels environmental imaginaries towards ecofascist narratives, and reinforcers extractive geopolitics and digitally mediated surveillance. The result is a global infrastructure that anticipates and disciplines climate-induced migration—particularly from the Global South—while facilitating planetary-scale resource extraction and national security agendas. Empowered by geo-spatial datasets and cloud infrastructures, the eco-algorithmic border moves towards paralysing genuine environmental and humanitarian activism, while further enhancing environmental and border imperialisms, and the profiling of counter-border actors and bordered subjects.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.024 |
| Scholarly communication | 0.010 | 0.014 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.004 | 0.003 |
| 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".