Sustainability Education: Inclusive Lens for Global Economy-an ESRAP Panel
Bibliographic record
Abstract
According to Henninger, Alevizou, and Oates (2016) fashion is subjective in nature. Thus, it is important to understand the definition and meaning of sustainability in the global marketplace. Additionally, consumers today have a growing awareness of practices that lead to sustainable living (Welters, 2015). Indeed, with an increased awareness of global concerns in the fashion industry through initiatives such as the UN’s Global Goals, Fashion Revolution, and the Copenhagen Fashion Summit there is a need to incorporate sustainability into apparel education programs.\nThe purpose of the proposed panel is to educate and increase awareness of the various global initiatives relative to sustainability in the apparel industry. In the U.S. we tend to teach in a microcosm, focusing on local or region-specific versus global needs. Thus, the objective of this panel is enhance the classroom experience through a cultural comparison of sustainable apparel practices throughout the globe.\nESRAP Panel: Rachel Eike, Erin Irick, Tara Konya, Young. A. Lee, Virginia Noon, Anupama Pasricha\nGuest Panelist:\nPanel Leader and Moderator: Tara Konya\nStructure: Panel discussion followed by audience engagement through open discussion or invitation\n1. Regions: In order to continue with this panel we will need to define regions. However, each macro-region could be broken down into micro subsections. For example, The US and Canada have a vastly different approach to sustainable. Similarly, the same can be said about Europe and Scandinavian Countries. North America Africa Asia Central America & the Caribbean Europe Oceania South America \n2. Key topics to discuss: According to the Copenhagen Fashion Summit and the Global Fashion Agenda (GFA) there are three core priorities for immediate implementation in the apparel industry: supply chain traceability efficient use of water, energy, and chemical respectful and secure work environments \nAdditionally, there are four transformational priorities for fundamental change: sustainable material mix closed-loops fashion system promotion of better wage systems the fourth industrial revolution. \nFor the purpose of this panel, we should focus on the four transformational change priorities, as the classroom is the first place to begin this change. Additionally, it is important to discuss the perceptions of each topic as well as region-specific issues related to each.\nNote: Each panelist identify a key topic area from the highlight transformational priorities list Identify a region that they will use to illustrate the topic area identified (example from that region based on their expertise and knowledge of the region) If you have other suggestions, feel free to add as comments. \nClaudia E. Henninger, Panayiota J. Alevizou, Caroline J. Oates, (2016) "What is sustainable fashion?", Journal of Fashion Marketing and Management: An International Journal, Vol. 20 Issue: 4, pp.400-416, https://doi.org/10.1108/JFMM-07-2015-0052\nWelters, L. (2015). The Fashion of Sustainability. In J. Hethorn & C. Ulasewicz (Eds.). Sustainable Fashion What’s Next?: A Conversation about Issues, Practices and Possibilities (pp. 4–26). London: Fairchild Books. Retrieved September 21 2018, from http://dx.doi.org/10.5040/9781501312250.ch-001\nO’Connor, T. (March 27, 2018) https://www.businessoffashion.com/articles/news-analysis/fashions-7-priorities-to-achieve-sustainability
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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 | 0.009 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.007 | 0.006 |
| Scholarly communication | 0.012 | 0.013 |
| Open science | 0.001 | 0.018 |
| Research integrity | 0.006 | 0.011 |
| Insufficient payload (model declined to judge) | 0.039 | 0.006 |
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".