Compost Kits: Bridging More-than-Human Theory with Design Practice through Vermicomposting
Bibliographic record
Abstract
This paper investigates how a vermicomposting kit designed as a probe, might make the more-than-human concepts of designing-with more accessible to design practices with materials. The probe is called the Compost Kits consisting of a vermicompost habitat and booklets to guide and prompt reflections on more-than-human concepts related to composting. The study includes four designers from diverse practices who used the Compost Kits for sixteen weeks. The designers co-speculated with us through the probes, interpreting their relations to the materials they use while maintaining the vermicomposting habitat alongside their practice. Results of our study include insights into the more-than-human biographies of their materials, and how matters of nonhuman agency and accountability relate to their design practices. Further, we discuss the challenges and opportunities for bridging the gap between more-than-human theory with practice.
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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 | 0.030 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.009 | 0.062 |
| Scholarly communication | 0.012 | 0.014 |
| Open science | 0.003 | 0.016 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".