Ecomodernism, cultured meat and the search for the ‘Middle landscape’
Bibliographic record
Abstract
Leo Marx's seminal work, Machine in the Garden: Technology and the Pastoral Ideal in America, continues to offer invaluable insights into the evolving relationship between technology and nature, particularly in contemporary debates surrounding food production. This paper examines the relevance of Marx's concepts to the discourse on cultured meat, a burgeoning biotechnological innovation in the food sector. By drawing on Marx's analysis of the tension between technology and nature, we explore how the cultured meat industry navigates this dichotomy, often echoing Marx's notion of the ‘middle landscape’ as a fusion of nature and technology. Through an analysis of discourses surrounding cultured meat, we elucidate how the industry constructs notions of nature and technology, and how these constructions intersect with the ideals of the ‘middle landscape’. Using examples from audiovisual and written promotional materials, we decode the cultural significance attributed to cultured meat and its implications for contemporary food imaginaries. Additionally, we contextualize the discourse on cultured meat within broader debates on ecomodernism, highlighting the socio-political dimensions of technological innovation in food production. By revisiting Marx's insights, this paper contributes to a deeper understanding of the complex social dynamics shaping contemporary food systems and environmental discourse.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.006 | 0.052 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".