Understanding illegal dumping in Ontario: Drivers, barriers, and policy recommendations
Bibliographic record
Abstract
Illegal dumping, the unauthorized disposal of waste in public spaces, poses significant environmental, social, and economic challenges, particularly in Ontario, Canada. This study investigates the drivers behind illegal dumping, with a focus on rural and urban communities in Ontario. Using a mixed-methods approach, including household surveys and interviews, we examine self-reported instances of dumping, attitudes towards waste management, and perceived barriers to legal waste disposal. The results reveal that inadequate waste collection infrastructure, particularly in rural areas, and high disposal costs are primary motivators for illegal dumping. Additionally, a lack of awareness regarding proper disposal methods exacerbates the issue. While most respondents recognize the immorality of illegal dumping, rural participants show less guilt and are more likely to engage in the behavior. The study provides actionable insights for policymakers, including the need for improved waste infrastructure, targeted educational campaigns, and increased enforcement efforts. By addressing these key factors, Ontario can mitigate the environmental and public health risks posed by illegal dumping, while fostering a culture of responsible waste disposal.
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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.007 | 0.004 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".