Rivers as borders? Navigating in‐between the tensions of water‐state‐society geographies
Bibliographic record
Abstract
Abstract What is unique about bringing rivers and borders into conversation with one another, and what are the implications for geographical research? This article and Special Section charts new directions in the study of rivers as borders. By emphasising a river‐centric approach, we collectively challenge traditional terra‐centric views prevalent in border research and show that rivers as borders are much more than just convenient tools for territorial demarcation and securing state sovereignty. The contributors engage rivers in conversation with border studies and conceptually navigate the liminal spaces in‐between the inherent tensions of fixity and flow by drawing on perspectives from cultural, political and environmental geography. River‐borders meander between land and water; violence and opportunity; artefact and landscape; dynamism and control. By bringing these multifaceted river‐borders and bordering practices into dialogue, we advance geographical understandings of what happens at the meeting point when rivers become borders. We argue that geographical research on water‐state‐society relations must analyse the relations between rivers' material agency and the differently entangled lifeworlds of border dwellers and crossers, considering their historical, material, cultural and social ties to the river.
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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 | 0.011 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.007 | 0.045 |
| Scholarly communication | 0.020 | 0.025 |
| Open science | 0.001 | 0.013 |
| Research integrity | 0.003 | 0.003 |
| 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".