Closing the loop of food systems: analyzing compost within community bounds
Bibliographic record
Abstract
The aim of this thesis is to construct a philosophical and ecological argument that places great value on localized food systems in terms of waste. This composition develops the claim that building composting infrastructure on a community scale will curb the global climate crisis and enhance the interconnectedness humans have within themselves, each other, and the natural world. The first prong of this thesis entails a theoretical framework we must function under in order to implement such radical food revolutions in our communities informed by Buddhist principles. This proceeds an international case study of the composting infrastructure of Fort Albany First Nation, Canada; Rwinkwavu, Rwanda; Havana, Cuba; Surabaya, Indonesia; and Dhaka, Bangladesh which emphasize the Indigenous, health-informed, war-related, colonial, and economic dimensions of compost in their respective communities. Following this, a national case study investigates community composting in Chicago, Illinois; Palmas de Mar, Puerto Rico; New York City, New York; Tucson, Arizona; and Sarasota, Florida under the lens of class, business, gender, education, and environmental justice within local food waste. The final case study of this thesis examines the systems of food waste within the author’s hometown and the work that she contributed to building food system resiliency in terms of compost in her community.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.007 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| 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".