Textile Society of America Symposium Program & Info: 2018: 16th Biennial; The Social Fabric: Deep Local to Pan Global
Bibliographic record
Abstract
The theme of TSA’s 16th Biennial Symposium is The Social Fabric: Deep Local to Pan Global. Located on the Pacific Rim, Vancouver offers a pertinent setting to probe the impact and influence of settlers and immigration on an already long-inhabited land, and how textile traditions have been influenced, changed, and/or adapted through and by cultural contact. In 2014 Vancouver city council unanimously voted to acknowledge that the city is on un-ceded Aboriginal territory, creating fertile ground for this conversation. We invite participants to examine textiles within the context of the “Deep Local,” defined as knowledge, beliefs, resources, and practices that are profoundly anchored in particular communities and places, which reflect not only the cultures of the original inhabitants but also those of later settlers. We encourage investigations that complicate as well as untangle relationships between people and their place, expressed in and through cloth. How is the Deep Local entwined in materials, processes, and objects that articulate cultural identity? How do textiles fuse and/or adapt in the “contact zone” to become Deep Local? We particularly invite presentations that examine difference and diversity as aspects of the Deep Local, the impact of cross-cultural contact including settlement and colonization, and how globalization both challenges and enriches the Deep Local. “Textiles as social fabric” allows for the inclusion not only of research of an historical nature but also investigations of contemporary artwork that reflects concerns for the Deep Local and the relationship between deep local and pan global. We welcome historic and contemporary perspectives that focus on disruptions to the social fabric of Deep Local textile processes by global pressures and products, including immigration. Program Publication Guidelines Location Keynote & Plenary Opening Reception Workshops & Tours Awards Banquet Dinner Exhibitions Gallery Hop Evening Evening at Granville Island Marketplace Sponsors & Donors
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.365 | 0.108 |
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".