Burn to harvest, burn to sabotage: Between fire and water on a sugar plantation in Madagascar
Bibliographic record
Abstract
Abstract Since 2009, the Chinese state‐owned corporation SINLANX has been managing the Anjava Sugar Plantation, previously managed by French, Malagasy, and Mauritian companies, in northern Madagascar. Built upon the infrastructure constructed by the French colonial regime and operating based on a collaboration agreement between SINLANX and the Malagasy state‐owned sugar company, Anjava presents a telling story of spatialized acts of survival and racialized conflicts over land and water in the interstitial spaces between capitalist production and subsistence economy. Malagasy villagers’ access to resources is often squeezed by multiple enclosures: a water‐delivery system and a land‐distribution system that prioritize sugar production and a bureaucratic system that punishes those who transgress the enclosures. Although Anjava villagers take advantage of the rhythm of sugar harvests and the nature of fire to sabotage sugar production or to make water claims for their livelihood, the agrarian and infrastructural arrangements at Anjava have perpetuated conditions of chronic precarity and profound marginalization of a landless population. The struggles at Anjava must be contextualized in the complex and ambiguous spaces between capital and labor, livelihood and resistance, dominance and adaptation, and ethnic collaboration and hostility.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.015 | 0.005 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".