Bibliographic record
Abstract
As food waste increases globally, many cities have implemented curbside collection of food waste (aka green bin programs) to divert food waste from landfills. However, not all municipalities in Ontario have green bin programs. A factor responsible for the adoption of green bin programs is the community support for the program. The study results are based on 407 completed surveys from randomly selected households in London, Ontario (a municipality without a green bin program) and Kitchener-Waterloo, Ontario (a municipality with a green bin program). Surveys were used to collect data to understand: i) the predictors of household green bin support and, ii) the difference in green bin support between both cities. Household food wasting and waste diversion variables were used to predict green bin support. As hypothesized, food wasting, and waste diversion variables were able to predict green bin support and Kitchener-Waterloo respondents were more supportive than those from London. Concern for environmental impact, convenience and norms favouring green bin use were the strongest predictors of green bin support in all three models (Kitchener-Waterloo, London and pooled sample). Composting, amount of food wasted, good provider identity, personal norms against food wasting, and food waste education were predictors in two models (London and pooled sample) while age was only a predictor one model (pooled sample). Municipalities looking to improve green bin support should consider educating their residents on food waste reduction and future research should investigate whether green bin support translates to green bin behaviour.
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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
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".