Critical Materiality
Bibliographic record
Abstract
The miniaturization of electronic technologies, as well as advances in organic and material science, have contributed to the development of composite, smart, and computational materials that create promising narratives for the future of ubiquitous computing. The goal of this one-day studio is to develop tools to acquire a deeper conceptual and critical understanding of materiality in HCI. The studio will draw on strategies from a broad range of sources including critical making, speculative design, experiential prototyping, and indigenous ontologies, in order to map out key questions and concerns. The studio will give the participants the opportunity to discuss the concept of Critical Materiality as a framework for developing tangible, embedded, and embodied interfaces, by brainstorming narratives around the past lives, current uses, and the future imaginaries of materials. Participants will co-develop a shared vocabulary and theoretical framework for Critical Materiality as a strategy to be deployed in conceiving and implementing HCI artifacts and experiences. The studio will culminate in the design and production of a deck of cards that propose keywords, questions, concerns, and opportunities for Hybrid Materials within this Critical Materiality framework. The deck of cards will be made available during the conference and distributed online.
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 | 0.013 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.010 | 0.039 |
| Scholarly communication | 0.015 | 0.015 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.022 | 0.004 |
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".