Mapping the Dynamics of the Vertical Farm: A Biopolitical Epistemology of Valuation
Bibliographic record
Abstract
In early 2020, Sobeys—one of Canada’s largest food retailers—partnered with Infarm Indoor Vertical Farming to install hydroponic vertical farming units in their retail locations. This partnership aims to build a resilient agri-food ecosystem in the face of climate change. Infarm is one of few vertical farming start-ups to reach ‘unicorn’ status in the recent boom of venture capital-backed urban farming solutions. Working to mitigate the climate crisis is critical, but I take venture capital as the spokesperson for green technologies as intuitively strange. Actor-network theory is a powerful tool for describing networks in which modern technical objects are embedded. By mapping the nodes of Infarm’s actor-network and analyzing how and by what the expertise of a ‘black-boxed’ vertical farm is negotiated, we can identify obligatory points of passage that render the epistemology of the network durable. Ultimately, Infarm’s vertical farming practice and its climate change mitigation logics are moulded by venture capital and its biopolitical epistemology of valuation—a way of thinking that conceptualizes ‘life’, biology and, therefore, the climate in terms of political economy.
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.003 | 0.033 |
| Scholarly communication | 0.009 | 0.020 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".