Sustainability Gets Thrown in the Trash: Comparing The Drivers and Barriers of Festival Waste Management In Canada and New Zealand
Bibliographic record
Abstract
Ten years ago, in 2012, the United Nations announced a global waste crisis. The festival industry produces a significant amount of waste; however, management practices and policies across locational contexts can help address sustainability goals. This study used Mair and Jago's model to understand the drivers and barriers experienced by festival organizers in Canada and New Zealand. Five key findings emerged from this study: (1) similarities in context, drivers, barriers, and catalysts exist across these two countries; (2) internal forces were generally more significant drivers than external forces; (3) waste management companies hold the potential to be a significant catalyst; (4) the most prominent barriers were a lack of resources and a lack of knowledge/awareness/skill; (5) political leadership as a contextual factor can support the adoption of festival waste management practices. Recommendations are put forth to leverage drivers and fill management and policy gaps in support of the United Nations SDGs.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 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".