Home Waste Audit: A Community Science Activity to Increase Waste Literacy and Reduce Household Waste
Bibliographic record
Abstract
Abstract The amount of household waste produced worldwide is increasing every year. In combination with other actions to reduce waste production and improve waste management, community engagement and community‐focused programs are needed to motivate the public to change their behavior in such a way that reduces their waste generation and increases the accuracy of waste sorting. It is also helpful for people to become more waste literate to empower them to be part of the solution. The Home Waste Audit (HWA) is a community science activity designed to increase waste literacy and reduce household waste. In the HWA, participants record their waste for a set period of time, research their local waste streams, and complete surveys to share perceptions of household waste habits. Here, we present data from a HWA conducted in 2021 as a case study. Before the audit, 60% of participants underestimated their weekly waste generation. Throughout the HWA, weekly waste count among households decreased by 31%. Participants found purchasing items with less/no packaging and avoiding single‐use plastics challenging. Easier changes included learning which items can/cannot be recycled and repurposing waste items. Several changes to waste habits were maintained 1 year after participation. These results demonstrate that the HWA is an effective tool for individuals to be a part of the solution by learning about local waste streams, reducing waste production, and accurately managing their household waste.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".