Who has the time? The temporality of tensions in the transboundary Red River basin
Bibliographic record
Abstract
We address the often absent “when” issues of governing shared rivers by focusing on timing and the challenges it creates for transboundary water management. Using the Red River basin and Chinese-Vietnamese relations as an example, this study illustrates how hydropolitical tensions are linked to the temporal scale. The politics of scale have been used widely in the Mekong region to critique particular framings of transboundary water governance and hydraulic infrastructure. These critiques are often geospatial, with less attention given to the temporal scale or the timing of water governance problems. The temporal scale is discussed in the Mekong region with regard to changes in seasonality, particularly around the arrival of monsoon rains. However, the “when” of water governance is not merely in response to natural phenomena; it is heavily mediated by social processes and infrastructure. The timing of infrastructure operations in transboundary water governance is in many cases at the core of hydropolitical tensions and risk. In the highly regulated Red River basin, the timing of hydropower operations and inherent temporal misfits create hydropolitical tensions across multiple timescales from seasons to seconds. Cooperation attempts reflect these temporal scale problems and are focused on reducing uncertainties around the timing of water governance processes. Drawing on insights from interviews in Northern Viet Nam, we analyzed the tensions caused by timing across infrastructure lifecycles and the “when” of water governance in the Red River basin between Yunnan, China and Northern Viet Nam.
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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 | 0.008 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.008 | 0.013 |
| Scholarly communication | 0.007 | 0.011 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".