Exploring the Feasibility of Zero Waste Approaches in Toronto’s Event Industry
Bibliographic record
Abstract
Events and festivals are integral to the City of Toronto’s culture as they bring various niche communities together, attract many tourists nationally and internationally, and are a source of income for businesses. Although all events are different, they all use a tremendous amount of resources and generate waste. As the world transitions quickly towards sustainable regulations and practices, the lack of educational opportunities that intersect between sustainability and event planning create an urgency for planners to receive the information needed to become successful in a fast-paced industry. Without proper guidance and knowledge, even the best intention event planners may miss the mark in terms of their sustainability targets, creating reputational risks for their organizations and skepticism of sustainability initiatives by event attendees. By applying a Zero Waste Framework, this research analyzes the challenges being experienced by various stakeholders through semi-structured interviews within the event industry to intervene at the highest level of the Zero Waste hierarchy – rethink and redesign. This research recommends a unique way to aggregate successful waste reducing strategies from different cities, organizations, and businesses in a feasible and low-cost way to maximize impact. The recommendations put forward aim to reduce barriers and increase benefits for planners to act toward sustainability and ultimately support Toronto’s TransformTO and Net Zero strategy.
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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 | 0.007 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.021 | 0.009 |
| Scholarly communication | 0.011 | 0.004 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".