Scottish reusable coffee cups: a multi-intervention CBSM benchmark analysis
Bibliographic record
Abstract
Purpose With litter from discarded single-use cups increasingly causing pollution, the purpose of this paper is to examine three intervention trials to encourage reusable cups to assess key success criteria and common barriers to successful implementation. Design/methodology/approach Using Lynes et al. (2014) Community-Based Social Marketing benchmark criteria, the authors qualitatively contrast three interventions using messy, citizen science data. Additionally, they provide a critique of the benchmarks themselves developing a new set of benchmarks to fit small organisations doing community-based social marketing. Findings Several benchmarks were obsolete and were unlikely ever to be met within the scope of these interventions. Important benchmarks needed to be highlighted further and additional benchmarks relating to key elements were added (product, engagement and stakeholders). Practical implications The authors provide practical suggestions to social marketers wishing to target single cup usage. This research highlights the need to not only carefully consider all benchmark criteria fully but also look beyond these as implementation issues are often the cause of limited success in these campaigns. Originality/value The authors focus on three interventions in open contexts and examine managerial/design aspects of this to contribute to the literature, while also critiquing and updating the benchmark criteria.
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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 | 0.044 | 0.075 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| 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".