Socio-economic futures for cellular agriculture: the development of a novel framework
Bibliographic record
Abstract
Novel agri-food technologies such as cellular agriculture present strong economic opportunities, with potential to reduce the environmental footprint of agriculture, improve animal welfare, and feed the world. A rich body of literature has emerged in the past five years that evaluates those claims, and illuminates the diverse food system futures framed by novel agri-food technology actors across the food system. To date, those characterizations of food system futures rely mainly on public data, such as technology advertisements and press releases, and have yet to engage deeply with a broader suite of social, economic, and material pathways for their emergence. The need for a robust social scientific framework through which to describe and evaluate concrete futures for novel food technologies such as cellular agriculture is needed. In this paper, we draw from a set of fifty-two interviews and 3 focus groups with key cellular agriculture stakeholders from industry, academia, investment, and research institutions. We found three key considerations for cellular agriculture futures: to understand the places and scales across which cellular agriculture ‘happens’, to balance competitive industry interests with public-private collaboration, and to navigate the extent to which cellular agriculture interfaces with traditional agriculture. From these considerations, we draw from the literature to deduce three dimensions across which to describe and evaluate concrete futures for novel agri-food technologies, broadly: centralization, access, and integration. Plotting food system futures across these three variables illuminates assumptions, preconceptions, and enabling conditions that may engender more or less desirable futures.
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 | 0.007 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.011 | 0.044 |
| Scholarly communication | 0.015 | 0.020 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.008 | 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".