Crafting a Fibre Scene in Cape Breton: The Tools, Technologies, and Motivations of the Unspun Heroes
Bibliographic record
Abstract
In the 21st century, spinning, knitting, and weaving are largely thought of as hobbies, pastimes, or small business activities. Despite the availability of mass-produced wool and fibre products, homespun and handmade products have seen a resurgence in popularity, partly because practitioner communities have developed. This article provides an ethnography of one such group, the Unspun Heroes in Cape Breton, Nova Scotia. Following a brief history of the group, the individually- and communally-owned tools and technology utilized within the Unspun Heroes is described. The forces that shape fibre artists’ access to tools and other resources of their craft in Cape Breton are identified, elucidating how strategies of shared, repurposed, and DIY tools enable fibre artists to sustainably engage in their craft. The motivations of members of the group are then considered, demonstrating how economic diversification strategies in Cape Breton have facilitated fibre arts, but are seldom the driving force for engagement in fibre arts and the Unspun Heroes group. In conclusion, the concept of “scene” is applied to the people, places, technologies, and connections described in this ethnography of the Unspun Heroes as a way of understanding the complex web of interactions and activities that plays out within and around the fluid membership of the group. This exploration demonstrates the innovative and entrepreneurial ethos of fibre artists in rural Canada.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.018 | 0.011 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".