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Record W4417453887 · doi:10.1108/jsocm-11-2024-0262

Scottish reusable coffee cups: a multi-intervention CBSM benchmark analysis

2025· article· en· W4417453887 on OpenAlexaff
Victoria K. Wells, Marylyn Carrigan, Kerry Mackay, Emma Glencross

Bibliographic record

VenueJournal of Social Marketing · 2025
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicGlobal trade, sustainability, and social impact
Canadian institutionsYork University
FundersBritish Science Association
KeywordsBenchmark (surveying)Scope (computer science)Psychological interventionKey (lock)Set (abstract data type)Intervention (counseling)

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.005
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.044
Threshold uncertainty score0.786

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.002
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.014
GPT teacher head0.289
Teacher spread0.275 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2025
Admission routes1
Has abstractyes

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