Probabilistic Rewards for Personal Mug Use
Bibliographic record
Abstract
Introduction Single-use disposable cups made or lined with plastic pose a significant environmental threat (Triantafillopoulos & Koukoulas, 2020; Foteinis, 2020). While previous research has found that surcharges on single-use plastic items can affect SUDC consumption and incentivise the use of personal (i.e., reusable) mugs (Sidhu et al., 2018; Poortinga et al., 2019), efforts to deploy such interventions on a population level have been ineffective (Vancouver City Council, 2023). The failure of surcharges on a broader scale underscores the need for novel interventions that incentivise personal mug use. Based on economic theories of gamification, the present study aimed to determine whether probabilistic rewards influence the use of personal mugs. Research Question How do probabilistic rewards influence the use of personal mugs? Methods Sales data was collected from two cafés on a Canadian university campus. One cafe (intervention café) implemented a 1 in 10 chance of winning free coffee for customers with personal mugs, while the other (neutral café) did not. Results At the neutral café sales made with personal mugs decreased between both control periods and the study period. At the intervention café there was a modest increase in sales in personal mugs between one of the control periods and the study period. Recommendations We propose that Loafe continue to implement the 1 in 10 chance of winning a free coffee for personal mug users with some slight alterations. Firstly, to address the issue of a lack of awareness of the intervention, we recommend 1) a longer intervention period, and 2) a more aggressive marketing campaign. Second, we recommend incorporating the raffle tracking system into the POS in order to limit the attentional demands placed on staff at the cafés. For the UBC Social Ecological Economic Development Studies (SEEDS) program we recommend continued partnerships with Loafe and other on-campus businesses. Moreover, based on existing gamification, we recommend future partnered studies incorporate some form of points system in order to encourage competition between customers. Disclaimer: “UBC SEEDS provides students with the opportunity to share the findings of their studies, as well as their opinions, conclusions and recommendations with the UBC community. The reader should bear in mind that this is a student project/report and is not an official document of UBC. Furthermore readers should bear in mind that these reports may not reflect the current status of activities at UBC. We urge you to contact the research persons mentioned in a report or the SEEDS Coordinator about the current status of the subject matter of a project/report.”
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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 | 0.004 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.016 | 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".