Vanguard Visions of Vertical Farming: Envisaging and Contesting an Emerging Food Production System
Bibliographic record
Abstract
Vertical farming is an emerging urban food growth proposal that has gained considerable attention for its ability to be space-efficient, independent of outside weather conditions, and to address a dismal agricultural system and ecoclimatic crises. VF is also a field riddled with debates on the unsustainability and high (energy) costs of a highly automated, indoor growth system that produces only a small range of perishable food. This paper explores arguments, visions, and internal disagreements among scientists, engineers, consultants, and entrepreneurs who form a heterogeneous, elite group of sociotechnical vanguards that popularize not yet widely accepted vanguard visions of future urban food production. It demonstrates that for the dominant vertical farm vanguard vision, a majority of vanguards borrow popular concepts and imaginaries from other sectors: containment of plant growth, cleanliness, the capability to feed the world, and the land-sparing narrative. The findings suggest three dimensions that add to the theorization of vanguard visions: the central role of mobilized problem-scripts; internal disagreements that indicate the contingency of vanguard visions and the existence of fringe visions; and that disagreements can reveal caveat politics, where a technical system, like VF, is not seen as the solution, but one of many.
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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 | 0.020 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.008 | 0.062 |
| Scholarly communication | 0.017 | 0.015 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.002 | 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".