Storying Monocrop Infrastructure: A Conversation on Governance, Scale, and Failure
Bibliographic record
Abstract
Plantations have recently become the focus of renewed empirical and conceptual inquiry across the social sciences, arts, and humanities. Scholarship in this interdisciplinary space calls on us to reckon with industrial monocultures’ enduring role in shaping contemporary structural inequalities, dominant technoscientific regimes, uneven divisions of labor, environmental violence, and struggles for justice, recognition, and repair. This Engagement piece contributes to these emerging currents by bringing into dialogue two scholars conducting research on monocrop systems in Latin America (Kregg Hetherington as interviewee) and Southeast Asia (Sophie Chao as interviewer). Anchored in Hetherington’s concept of “agribiopolitics,” the interview approaches monocrops through the two interrelated themes of governance and failure. Governance brings us to consider the forms of control, management, monitoring, and accountability that undergird agribiopolitical regimes, the institutions, practices, and mechanisms that make them possible, and the structures of exclusion, oppression, and violence on which they often depend. Failure brings us to attend to the limits or tipping points of governance as system and process—it’s rough edges, its unexpected failings, its uneven distribution, and how failure can be both productive and an opportunity for flight. In reflecting on ways of storying monocrops otherwise, we invite theoretical and methodological dialogue around the form and effects of anthropocenic infrastructures more broadly across the fields of science and technology studies, anthropology, critical race studies, political ecology, agrarian studies, and the environmental humanities. This interview is a revised and expanded version of an Author- Meets-Critic conversation that took place at the Society for Social Studies of Science, (4S) meeting in Cholula, Mexico, where Hetherington’s monograph, The Government of Beans, received the 2022 Rachel Carson Award.
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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 | 0.016 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.026 | 0.058 |
| Scholarly communication | 0.013 | 0.030 |
| Open science | 0.003 | 0.015 |
| Research integrity | 0.010 | 0.017 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".