Geo-Logics of Power: Disaster Capitalism, Himalayan Materialities, and the Geopolitical Economy of Reconstruction in Post-Earthquake Nepal
Bibliographic record
Abstract
The 2015 earthquakes in Nepal killed more than 9,000 people, displaced millions of people and deeply affected the economy. The earthquakes and reconstructions processes also transformed Nepal into a complex terrain of geoeconomic accumulation and geopolitical manoeuvring, including major international capital flows, the promulgation of a new constitution, an economic blockade by India and the expansion of trade corridors with China. Building on critiques of ‘disaster capitalism’, we propose and mobilize the concept of ‘geo-logics of power’ to draw further attention to the materialities of geopolitical and geoeconomic processes shaping reconstruction in post-earthquake Nepal. Focusing on two trans-Himalayan corridors connecting Nepal and China, we argue that the Nepal experienced a particular form of disaster capitalism: one in which the geo-logics of power – including trans-Himalayan discourses, practices, and materialities – came to shape political and economic transformations of a country long portrayed as a ‘buffer’ state between Indian and China. More broadly, we suggest that geo-logics of power result from a combination of geopolitical and geoeconomic power dynamics informed by geological formations and associated socio-natural processes.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.022 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.001 | 0.006 |
| 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".