An Aesthetics of Consumption: How Trash Becomes Transformative through Participatory Arts-Based Research
Bibliographic record
Abstract
At a time where the scale of our environmental challenges is becoming increasingly evident and our fate is at risk, human beings’ collective ability to effect the changes that could mitigate consumptive behaviour is still our most significant hurdle. This research explores the potential of a participatory environmental art project using interviews and dialogue to inquire into if and how an arts-based education approach affects participants’ perceptions and emotions, environmental awareness, and behaviour. Inspired by Gregg Segal’s photographic project “7 days of garbage,” this research is designed using an arts-based educational approach focussing on the topic of consumption, habits, and waste management. It was conducted in the Spring of 2017 with families from the Montréal area who collected their garbage two weeks, were later aesthetically photographed with it, and subsequently interviewed about their experience and resulting insights. The study follows a qualitative approach, combining arts-based methodologies and narrative inquiry.
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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 | 0.013 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.017 | 0.058 |
| Scholarly communication | 0.014 | 0.008 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 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".