Human Folly and Border Fences: Looking to Non-Human Actors at the Indo–Bangladesh Border
Bibliographic record
Abstract
The obsession with inter-state territorial borders and the associated paraphernalia of border management and security makes borders and their management a primarily human-centric discourse. This paper makes an attempt at introducing the agency of rivers as non-human actors—or rather as actants—in shaping and managing international borders. The paper looks specifically at the riverine sector of the Indo-Bangladesh border, where the international boundary has been re-negotiated each year by the transnational rivers, primarily the Brahmaputra (also the Gangadhar), through flooding, erosion, and deposition of sediment. By interrogating the role of rivers in shaping the border and border management strategies, the paper argues that humans, despite persisting as the primary agents in border management, are not the only actors. Drawing on Actor Network Theory (ANT), a case is made to appreciate the general symmetry between humans and non-humans as a-priori equal. Incorporating both in an actor-network may provide insights into border management in complex borderlands.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.007 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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".