Capturing Compostables: A Case Study of Small Scale Composting in Vancouver
Bibliographic record
Abstract
This research study explores the role of small-scale composting for the processing of food scraps in the City of Vancouver within the nexus of organics diversion and the municipality’s Greenest City policy goals. Conceptually the study is informed by the integration of public policy, the policy cycle, and concepts relating to diversified, scalable technology. Empirically the study adopts a soft systems methodology that employs an inquiry-based approach, predicated on in-depth semi-structured interviews with 27 individuals active in organic waste management within the City of Vancouver and the Metro Vancouver Region. Interviews were stratified into three groups to: reveal regional policy impediments; identify drivers for small-scale composting; and describe the operations and challenges experienced by City small-scale composting operators. This study found that City and Regional policies continue to be formulated and implemented to prioritize the development of large-scale organic waste collection and processing facilities. However, small-scale composting continues to gain momentum, as it presents an opportunity to diversify the scale and geographical placement of organic waste processing. Small-scale composting also connects directly to Greenest City policy outcomes for low-carbon waste management, local food production, increased awareness and behaviour change, and the potential to create low-barrier, green jobs. Findings suggest that, as innovative models for small-scale composting emerge, current regulatory requirements are prohibitive for the establishment of systems that aim to process food scraps from multiple City sites, and need to be adapted to meet the evolving options for diversified organics management. To further ensure the viability of small-scale composting, new policy development is needed to create a regulatory framework that is conducive for encouraging innovative methods of capturing and processing compostable organics.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.008 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".