Gen Z Recycling During COVID-19: Are We in It Together?
Bibliographic record
Abstract
The global annual waste generated every year is more than two billion tonnes (Statista, 2025), and when this waste is disposed of in landfills, it adversely affects many people’s lives. Recycling can not only reduce the amount of waste sent to landfills, but reusing resources is also good for the environment (Biswas et al., 2021, Waste-Wise Cities: Best Practices in Municipal Solid Waste Management ). While recycling has been around for some time, there is scant research on whether Gen Z’s recycling behaviour, in particular, has become a habit and is therefore here to stay or not. The global pandemic caused by COVID-19 provides an apt backdrop to evaluate whether or not recycling has become a way of life for Gen Z. This study is based on a survey conducted through a questionnaire that was administered to undergraduate students. With the onset of COVID-19, more effort is being spent on understanding the importance of public health and how one person’s well-being affects another person in a neighbourhood. Gen Zs, who arguably constitute a significant group of decision-makers that will affect the future of recycling, will determine the success of future recycling endeavours. Therefore, understanding the decision-making of this group will help in advancing research that specifically studies how a nation’s sustainability agenda can be strengthened.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".