Seeing Raw Fibers: Collaborating with Fiber Farmers to Develop Tacit Knowledge in a Fiber Sorting, Grading, and Classing Apprenticeship

Abstract

In an effort to dig deeper into slow fashion in a local context, this case study research draws attention to the quality of raw fibers to inform clothing and textile design practices. A unique standardized system of sorting, grading, and classing raw fibers is developing in the United States to help fiber farmers with small flocks, and a variety of fiber animals such as sheep, alpacas, and goats. The system aims to help farmers learn about fiber quality, develop higher quality products, and earn more sustainable incomes for their farm businesses. This research is an outcome of taking Basic and Advanced Fiber Sorting, Grading, and Classing courses, and doing the Fiber Sorting, Grading, and Classing apprenticeship. It uses Actor Network theory as a framework to outline the practice-based approach that requires continuous “reflection-in-action” with a variety of natural fibers. The objectives of this study were to (1) gain hands-on experience sorting and grading raw animal fibers sourced directly from farmers, and (2) obtain feedback from a Master fiber mentor to learn from the process. This study presents results of completing approximately 30% of the apprenticeship program with over 70 alpaca, wool, and mohair fleeces from five fiber farms during Winter 2016, Spring, and Summer 2017. A majority of the fleeces, 60, were sorted in direct collaboration with a fellow fiber apprentice who is an alpaca farmer. The accuracy of sorting and grading was 73% and the process led to tacit knowledge with natural fibers. This study presents an Actor Network Theory analysis diagram to visualize the fiber apprenticeship process. Overall, this study provides deeper insight into assessing fiber quality to determine optimal clothing and textile designs for slow fashion localism.