Why don’t we reuse our food packaging? Insights from two organizations implementing packaging return systems to avoid single-use plastics
Bibliographic record
Abstract
Abstract Plastic pollution is a pervasive and urgent environmental issue, caused by our unsustainable use of single-use plastics (plastic items that are commonly discarded after one use). Many of these plastics are used in food packaging, frequently ending up in the environment. A potential solution to this problem is packaging reuse systems, meaning systems to incentivize consumers to return used packaging for refill (by charging a deposit) or appealing to environmentally conscious consumers to bring in their own packaging for shopping (e.g., in zero-waste stores). Deposit return systems (DRS) are well-established in several countries; however, they are often used for single-use packaging with the purpose to improve the recycling rate of plastic packaging (and therefore do not focus on reuse). Further, DRS mainly apply to beverages, not solid food containers. Nevertheless, they are well-studied systems, highlighting key concerns for the implementation of innovative solutions to keep packaging waste out of the environment (e.g., aspects of hygiene, transport and logistics, brand identity and consumer behavior). In this study, we explore how packaging reuse systems are implemented for solid and semi-solid food products by two organizations (one in Canada and the other in Germany) concerned with reducing plastic waste in the food sector.
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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 | 0.007 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.009 | 0.006 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".