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Record W4400485460 · doi:10.1080/15378020.2024.2377435

The impact of self-directed learning on reducing single use plastic in back-of-house restaurant operations

2024· article· en· W4400485460 on OpenAlexaffabout
Emily Robinson, Simon Somogyi, Bruce McAdams

Bibliographic record

VenueJournal of Foodservice Business Research · 2024
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicFood Waste Reduction and Sustainability
Canadian institutionsUniversity of Guelph
Fundersnot available
KeywordsMarketingBusinessAdvertisingOperations managementEconomics

Abstract

fetched live from OpenAlex

The purpose of the study was to determine the barriers to single-use plastic (SUP) reduction, specifically in the back-of-house (BOH) of restaurants, based on preexisting attitudes and perceived barriers. The associated psychological barriers were reflected upon in the context of adult learning theory and self-directed learning, as a novel setting in which to apply these theories. This study assessed the barriers to SUP reduction in restaurants by conducting fourteen qualitative interviews with small- to medium-sized restaurant operators in Canada which were digitally transcribed verbatim and analyzed using thematic content analysis. The study found that the most common barriers to reducing SUPs in the BOH are: cost, lack of time, and suppliers. The study also finds that there is a disconnect between the participants’ attitudes toward SUPs and their perceived barriers, and that there is a lack of access to resources for learning as a barrier. This study adds to self-directed learnings theory’s influence and limitations on sustainability behaviors and the application of self-directed learning to a unique setting. It also concludes that the restaurant BOH environment is akin to a self-directed learning environment. This study has implications for restaurant owners and operators attempting to reduce their SUP consumption.

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)0.002
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.922
Threshold uncertainty score0.368

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.003
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.079
GPT teacher head0.351
Teacher spread0.271 · 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

Citations3
Published2024
Admission routes2
Has abstractyes

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