Reframing human excreta management as part of food and farming systems
Bibliographic record
Abstract
Recognition of human excreta as a resource, rather than as waste, has led to the emergence of a range of new and innovative nutrient recovery solutions. Nevertheless, the management of human excreta remains largely rooted in current sanitation and wastewater management approaches, which often makes nutrient recovery an add-on to existing infrastructures. In this paper, we argue that framing human excreta management as a resource recovery challenge within waste management obscures important trade-offs. We explore the factors that would be brought to the fore by reframing human excreta management as part of food and farming systems. We find that such a reframing would accentuate (at least) six aspects of critical importance that are currently largely overlooked. Recognizing that the proposed framing may also have its limitations, we argue that it has the potential to better guide human excreta management towards long-term global food, soil, and nutrient security while reducing the risk of compromising other priorities related to human and environmental health.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".