The Future of More-Than-Human Design: A Computing Practice in Crisis?
Bibliographic record
Abstract
Given the current ecological crisis, the HCI and design community is showing a growing interest in the adoption of more-than-human perspectives, challenging human-centric approaches.While this has sparked numerous research initiatives, many of them are still a far cry from providing practical solutions or transforming the industry.This also presents a hurdle for teaching more-than-human perspectives to design students, as they may feel powerless to practice those teachings in real-life industrial settings.To bring forth concrete examples of how more-than-human design practice can matter, we believe that it is now time to move beyond theorising about and advocating for the adoption of such perspectives and start a morethan-human design practice that transforms the industry.This workshop therefore aims to bring together educators, researchers, and designers to discuss and co-develop strategies for transitioning more-than-human perspectives from niche/speculation to mainstream/practice in HCI and design.The workshop also aims to develop ways to empower students to work with these perspectives to bring about this transformation of the industry.
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 | 0.068 | 0.041 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.013 | 0.074 |
| Scholarly communication | 0.030 | 0.045 |
| Open science | 0.005 | 0.018 |
| Research integrity | 0.013 | 0.019 |
| Insufficient payload (model declined to judge) | 0.014 | 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".