Eco-Wearables: Merging Art and Technology for Environmental Crisis Awareness
Bibliographic record
Abstract
Eco-Wearables focuses on global warming, the ways that the fashion industry contributes to climate change, and the ways that fashion can also help consumers to have environmental awareness. The contemporary environmental crisis poses significant threats to global ecosystems, necessitating proactive measures to mitigate its undesirable effects. The fast fashion trend plays a significant role in Earth's warming. According to some estimates, the fashion industry is responsible for 10% of humanity’s carbon emissions, water consumption, and waste production, leading to unprecedented temperature rises (UNECE, 2018). The aim of this research is to explore the potential use of wearable technology as a medium for visualizing the intensity of environmental crises and depicting temperature fluctuations. This project designs and creates an interactive garment centered on Canada, one of the significant contributors to Earth's warming, and victim to its effects, through forest fires. By designing a garment with precise laser-cut patterns inspired by Canadian provinces, it endeavors to dynamically visualize data that represents the challenges of a warming planet. The garment statistically shows the wildfire data in different Canadian provinces, transforming them into visual and wearable cues, that is a number of LED displays and colour changes based on equal intervals. Heating pads warm the garment, and these are driven by temperature increases in each province, making the climate crises tangible. By exploring global warming and its effects like Canadian wildfires and the impact of the fashion industry I intend to provide a comprehensive and holistic perspective on the complex web of issues surrounding climate change.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.010 | 0.011 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.020 | 0.003 |
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".