Engaging With Non-Human Perspectives Through Embodied Design Practices
Bibliographic record
Abstract
More-than-human perspectives become increasingly important with the growth of global challenges and the intervention of autonomous technologies. Research has elaborated on the rationale for more-than-human-centred design approaches but still needs more practical guidance to embody non-human perspectives in the design process. This paper addresses embodied co-creation practices that enable the involvement of human and non-human actors in the design process. The estrangement framework provides a lens for comparing three examples from the literature. The evaluation of these three different co-creation projects reveals considerations on how to alter the human way of designing to be more inclusive of non-human perspectives. In this process, relationships and agency between human and non-human co-creators dynamically change and unfold. Their entanglements impact the collaborative design process in different ways. Humans and non-humans’ diverse perspectives and vulnerabilities become transparent and facilitate the negotiation of their individual values and needs. Embodied design practices can translate the theoretical considerations made in this paper into tangible design activities. The performing arts could provide a laboratory to experiment with such co-creation processes through artistic means—to understand better non-human perspectives and how humans and non-humans co-shape their relationships in and with the world.
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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 | 0.028 | 0.017 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.005 | 0.027 |
| Scholarly communication | 0.015 | 0.013 |
| Open science | 0.003 | 0.017 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".