Impacts of arts-based Ecopedagogy in sustainable residential food waste management
Bibliographic record
Abstract
How can arts-based ecopedagogy impact sustainability in organic waste management?In Winnipeg, Canada (population 841,000), there is no publicly-funded program for the 34 million kilograms of Residential Food Waste produced annually, nor is there an industrious environmental education program to support it.The consequence of inaction is the increase of greenhouse gas emissions from untreated waste, further threatening global warming, especially an issue for the disadvantaged urban population, and those in multi-family dwellings such as condominiums/apartments.This paper outlines a research proposal set to commence in 2024/2025.A suitable framework model chosen for this critical environmental exploration was ecopedagogy, a transformative teaching in which researchers' problem-pose the politics of socio-environmental connections through local, global, and planetary lenses.Arts-Based and Participatory Action Research will follow ecopedagogy as an innovative and mutually supportive multidisciplinary and methodological approach to knowledge-building with the creative arts at its core; useful not just for inquiry and learning, but also to challenge dominant ideas, hegemony, oppression, and ideologies through a critical lens.A practical guide and example for the effective methods of Participatory Video under the framework of ecopedagogy will be displayed to enable research participants as active co-researchers.All humans have a right to live in a clean, healthy and sustainable environment, based on the resolution that was unanimously signed by the United Nations General Assembly, 28 July 2022.Is this creative approach of arts-based ecopedagogy right for you?
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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 | 0.008 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.012 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.016 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 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".