The Soil Food Web: Notes towards Cultivating "New" Alliances between Earthlings
Bibliographic record
Abstract
How can we grasp our relationship to the Earth through the earth? In dealing with the human and its relationship to the soil food web, this lecture traces translations as they occur through the sense of smell and taste using Thomas A. Sebeok's concept of the chemical sign. What world do we share with nonhumans when we cultivate the soil? Employing Felix Guattari's pseudo-narrative detour through myth, ritual and science and Gilles Deleuze's elaboration of uncertainty in The Logic of Sense, the lecture criticizes subject-centred encounters with the soil food web and its complexity, aiming instead for an anexact science of Earth-earthling relations. It advances the notion that the mundane eating and defecating tendencies shared by all earthlings is the basis of a renewed material alliance with the Earth. Though the Earth and its earthlings share what may be called flesh (organic and nonorganic), it is what passes through and is exchanged by flesh that is of interest here. Moving from retracing the new assemblies of plants, animals, and microbes that are produced through cultivated efforts at rearranging the mundane (spatial questions), the lecture closes on an exploration of the differing layers of time (Chronos and Aion) bound up in the new assemblies.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.013 | 0.022 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 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".