Pollination and the Horrors of Yield: Scarcity and Survival in the Glasshouse
Bibliographic record
Abstract
Pollination is a lynchpin anchoring multispecies survival, but in the racial capitalist configurations of condensed environment agriculture, pollination politics are informed by life-depleting and carceral logics, as well as imaginaries steeped in racial hierarchies and sex binaries that ground future survival in colonial, heteropatriarchal, and normative terms. This article argues that pollination politics are focused on intensifying agriculture and increasing yield within carceral infrastructures, and that the orchestration of pollination is governed by socio-sexual schemas about fitness and fecundity that are governed by a normative reproductive futurism. Joining feminist STS and queer theory, the article traces how forms of life in greenhouses and other agricultural infrastructures are “gardened” in the interests of modes of sustainability that are fundamentally exploitative. Biodiversity is domesticated and depoliticized, and all forms of human and non-human vitality are directed towards increased yield. The naturalization of sexual difference influences how plant life is managed, but also how temporary foreign labor is biopolitically managed in controlled environment agricultural infrastructures. The article reads the perverse politics of scarcity through the South African film Glasshouse (2021) and ends by speculating on how “wild pollination” might present more decolonial, anti-racist, queer, and liberatory sustainable futures.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.003 | 0.013 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".