Borders in Motion: The River Border <i>Chars</i> in South Asia
Bibliographic record
Abstract
In South Asia, 54 rivers crisscross the India–Bangladesh border. Many amongst them shift continuously, posing a challenge in locating/marking the border. This commentary examines two such shifting rivers, the meandering Ganga and the braided Brahmaputra, to explore how border-making processes unfold in landscapes marked by continuous fluctuations. While borders are typically imagined as fixed, it examines how they operate along the course of these shifting rivers. We interrogate the notion of fixity (border) in a fluid scape by situating ourselves in the impermanent river islands (chars) along these river borders. Noticeably, the chars themselves are temporary and unstable formations of land and water, which further complicate river border demarcation. Moreover, we analyze how fixed political markers, when placed in shifting landscapes due to lack of policy initiatives spark contestation between the state, its agencies, and local people living along river borders, an aspect less highlighted in South Asian border studies.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.009 | 0.013 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 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".