Management Perspectives on Plastics Free Sport Facilities’ Beverage Service
Bibliographic record
Abstract
Due to a global environmental issue where plastic waste gets into our water resources, this research identified management perspectives on the implications of moving to plastic-free beverage services at sport facilities. The focus encompassed implications of both eliminating plastics and the introduction of biodegradable alternatives. Semi-structured interviews were conducted with a purposeful sampling of expert food and beverage managers employed at sport facilities with Canadian Hockey League tenants. Interview questions were developed using Transition Management Theory (Kemp, Parto & Gibson, 2015), Attitude-Behaviour-Gap (Jacobs et al., 2018), concepts of consumerism (Koskijoki, 1997), and the call for sustainability business models (Borgert et al., 2018). Using thematic analysis, this study accessed the nuanced understandings of plastics use and the implications of implementing biodegradable alternatives. Results revealed inconsistent environmental management strategies: that government mandates are key; that there is a lack of public pressure concerning plastic waste management pratices; that bioplastic options are overlooked; that seven barriers impact the use of plastic alternatives; and, that mitigation and management of plastic is not their management role. In conclusion, much work is needed to move towards eliminating plastics and the introduction of biodegradable alternatives at sport facility concessions.
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 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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.012 | 0.009 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".