Industrializing Bacterial Work: Microbiopolitics, Biogas Alchemy, and the French Waste Management Sector
Bibliographic record
Abstract
Biological waste recycling has recently attracted widespread interest and investment. Large industrial plants that use microbiological engineering to process municipal waste and produce biogas have been established in different countries including Germany, France, Portugal, Brazil, Canada, and China, to name a few. These biowaste facilities are not simply classical energy infrastructures, as they are commonly described, but rather rely on the power of bacteria, archaea, and fungi at several levels to accomplish the work of waste metamorphosis. Such an appropriation of microbes’ vital force is based on specific and complex human–microbe relations, or microbiopolitics, that rely on practices of attention, care, and proximity with waste material. However, in these industrial attempts of upgrading the metabolic work of bacteria, the need for more hands-on daily care of waste materials and biological processes is being superseded by the automation of waste processing. Close examination of the French context shows that this shift produces ignorance regarding the growth and evolution of bacterial colonies and reduces humans’ attention and proximity to the industrial process, thereby depriving the microbes of elements that hitherto kept them domesticated.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.009 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| 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".