Bibliographic record
Abstract
On the precipice of space exploration, smart fabrics, biochips and exoskeletons, the materiality of our wearable and body-centric future is a critical and political issue. If we are to develop technologies to take us to new places, we should be able to imagine radically different futures. The materials, tools and supply chains available to us seed the future we can build and are fuel for the possibilities we can imagine. Textile-based Do It Yourself (DIY) electronics have been suggested in Maker technology circles as an alternative path to electronics to broaden the diversity of those imagining our future, but they are still heavily divided by lines of gender, and hindered by supply chain availability. There are many textile-based technologies that have unique technical qualities to offer, but their development is stifled by systemic issues. This article suggests that e-textile technologies are a result of an entrenched system of power and act as a control method over the vision of the future rather than the suggested notion that they are an avenue of exploration. Using Erbu Kurbak’s concept of Lost Futures, and Elizabeth Ryan’s interoperation of wearable technology as immaterial labour, I make the case for e-textile practice being a trap and never equal contributor to the technological discourse.
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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.028 |
| Scholarly communication | 0.016 | 0.016 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.012 | 0.003 |
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".