Bibliographic record
Abstract
Abstract CloTHING(s) as conversation is a research initiative that seeks to revising textile product assumptions. Clothing and specific combinations of articles worn on the body are often understood and referred to as a statement. The notion of clothing as statement is prevalent in catwalk culture (the fashion statement). It is equally evident in the clothing choices of individuals who do not participate in the fashion schema, be it through regulated attire (uniforms, professional garb, etc.) or as part of counter culture (punk, hippie, hactivists, etc.). This project posits that new possibilities may be envisaged by reframing the understanding of clothing from a unidirectional entity to one of multidirectional interchanges and dialogue. An interdisciplinary team made up of designers, an engineer and a weaver are exploring possibilities inherent in the act of conversation. Material- and form-based studies, generative and cultural probe techniques, and in situ explorations are being used to explore and propose: alternate means of thinking about the role of clothing (from statement to conversation); alternate means of communication, and alternate means of production and use. Nascent developments are provoking discussions about persistent habits of making, distribution and exchange connected to the garments we wear.
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 | 0.005 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.012 |
| Scholarly communication | 0.011 | 0.009 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 0.002 |
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".