Framing Waste as a Common Treasury to Facilitate the Circular Economy
Bibliographic record
Abstract
The unsustainable amounts of waste currently produced are hugely problematic – resources are being ‘wasted’ and there are numerous issues related to current waste management operations. Many different laws and policies have been introduced to tackle the ‘wicked’ problem of the existing waste crisis, including on the circular economy in which wastes and resources are ‘looped’ back into systems through reuse, recycling, and other recovery to promote sustainable development. Yet, these have neither had the desired nor required transformative effect. This chapter therefore examines recasting waste as a common treasury that may, combined with other existing laws and policies, incentivise the prevention and sustainable circular management of waste. Malcolm and Clarke’s understanding of the concept of common treasury to describe the physical nature and importance of a resource, rather than its legal nature, is adopted in this chapter. The aim of this chapter is two-fold. First, the possible benefits of reconceiving waste as a common treasury are evaluated. Second, the impacts of the private, communal, state, and no property in waste are examined to identify the most appropriate property scheme for waste to operate as a common treasury. This chapter therefore contributes to furthering arguments introduced by Malcolm and Clarke on the notion of common treasury, and their consideration of the most fitting property interests, by investigating the particular example of the man-made resource of waste in contrast to their examination of a natural resource, water
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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 | 0.006 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.007 | 0.030 |
| Scholarly communication | 0.013 | 0.017 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.007 | 0.008 |
| Insufficient payload (model declined to judge) | 0.008 | 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".