Can the river speak? Epistemological confrontation in the rise and fall of the land grab in Gambella, Ethiopia
Bibliographic record
Abstract
In this paper, I focus on the role of knowledge production in the rise and fall of the Indian multinational agribusiness firm Karuturi’s efforts to become a leading global supplier of food through the initiation of large-scale industrial agricultural production in the Gambella province of Ethiopia. In particular, I interrogate a modernist epistemological framework which privileges the “developmental” knowledge of the Ethiopian state and the “productive” knowledge of Indian capital as central to the urgent task of mastering nature and bringing dormant virgin lands to life, while at the same time it necessarily discounts, through processes of racialization, displaced indigenous peoples and nonhuman life forms as beings incapable of efficient and productive economic activity. My argument in this paper is that while modernist knowledge production and mobilization has been critical to Karuturi’s construction of the Gambella land concession as a staging ground for its launch into global prominence in agro-food provisioning, it has also proved fatal to the project, as the epistemological inability to incorporate indigenous knowledge that accounts for “extra-human” agency left the company dramatically unaware of the particular socio-ecological dynamics of the Baro River ecosystem on whose floodplain the land concession was located.
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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 | 0.005 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.024 | 0.047 |
| Scholarly communication | 0.012 | 0.008 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.004 | 0.006 |
| 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".