Recycling behaviors and knowledge in Western Newfoundland
Bibliographic record
Abstract
Western Newfoundland, the scope of our chapter, has a mandatory recycling program for single-unit households. As in other areas of Canada, there are ongoing challenges with contamination (i.e., garbage in recycling bags or recycling in garbage bags), hence the need for behavior analysis among residents, especially as the region has not previously been examined in the literature. We administered a survey to Western Newfoundland residents to understand noncompliance with recycling guidelines and explore ways to increase compliance. The results reveal high variability across respondents, both for their self-reported amount of recycling and for their revealed knowledge about how to recycle different items. In this region, technical-organizational factors (e.g., access to services, distrust in the system) appear more salient than sociopsychological factors (e.g., responsibility to future generations, wishful recycling) or sociodemographic factors (e.g., gender, income), but all three categories play a role in explaining behaviors and perceptions. A variety of potential changes are identified by the study—expand range of materials, improve availability and convenience, lower fees, strengthen enforcement—but two education-themed recommendations emerged as cross-cutting throughout the results: expand available information on what items go where and distribute transparent information on what happens to items after they are collected.
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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 | 0.000 |
| 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 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.007 | 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".