The impact of self-directed learning on reducing single use plastic in back-of-house restaurant operations
Bibliographic record
Abstract
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.
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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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".