Leveraging single-use plastic to foster a sustainable community: Empowering Toronto communities to reduce and reuse plastics through the co-creation of pro-environmental initiatives
Bibliographic record
Abstract
While many people in Toronto believe it is important to support sustainability initiatives, they are not always sure how to make a true difference as an individual. Recycling is one act that Torontonians do participate in, but most are unaware of the fact that until recently, the majority of their recycled materials were being exported to be dealt with by China. In December 2017, China stopped importing most of those recycling materials, which are now ending up in Canadian landfills instead. Some of the most problematic materials are plastics, with 86% of them ending up in landfills in Canada. This MRP set out to find alternative solutions for the Toronto community beyond recycling by answering the following HMW statement: How might we ignite a community to reduce and reuse household plastic containers by designing processes that can be easily adopted and acted upon by the Toronto communities and its members? To explore this, a prototype in the form of a co-creation community workshop was developed to encourage and foster pro-environmental behaviour changes using the Design Thinking framework. In addition, the workshop was leveraged as a method to conduct further Research through Design (RtD) in order to uncover further insights to help iterate on the solution. After the series of workshops, there was a positive increase in attitude and behaviour changes toward reducing and reusing plastic containers, at least in the short-term. In order to foster long-term, permanent behaviour changes among community members, a Theory of Change (ToC) for plastic consumption in Toronto was created. This ToC is meant to be utilized by Toronto communities as a guide and starting point to help iterate upon the workshop series and to generate new scalable solutions to ignite behaviour changes. This MRP acknowledges that individuals of small communities alone cannot change the system of plastic waste management. However, testing and co-creating with communities is an effective way of prototyping before investing in a scaled-up solution. More importantly, local grassroots movements from communities do have the power to influence and impact larger key stakeholders and areas of the waste management system. The ToC that was created can help empower both individuals and communities to care about and demand systemic changes, both directly and indirectly.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.011 | 0.008 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.002 | 0.002 |
| 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".