Bibliographic record
Abstract
This essay considers a design student project that practised methods and languages for connecting designers with the more-than-human. The term refers to ecologist David Abram’s phrase “the more-than-human world” as a way of considering our entanglement with earthly nature. Alongside ecological thinking, we drew upon New Zealand Māori and Canadian First Nation scholars and worked with local cultural advisors. This project was offered across two design classes, beginning with one in Vancouver, Canada, and the other in Dunedin, New Zealand, with a six-week synchronous window. These schools connected through DESIS, a network of labs that research social innovation and sustainability. Teachers and students were able to share their approaches and progress online, culminating in an online exhibition and conversation. Key ideas explored included Karl Wixon’s “whakapapa-centred design” and Robin Wall-Kimmerer’s “grammar of animacy,” indigenous values that centre the natural world as sentient and present with the world of humans. Students were tasked with becoming advocates for a more-than-human being. They drew upon multiple frames to learn about this being and conceive of possible different relationships. After conducting a series of immersive research activities including meditative, phenomenological and academic approaches, students drew upon their design skills to translate and share their learning. The outcomes are a variety of prototypes, designed to share this advocacy as experiences for others. Through a combination of online and embodied learning, many explorations brought to light understandings about the fundamental interconnectedness of humans with the earth, with all species and with each other.
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 | 0.010 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.011 | 0.032 |
| Scholarly communication | 0.016 | 0.015 |
| Open science | 0.003 | 0.016 |
| Research integrity | 0.005 | 0.012 |
| Insufficient payload (model declined to judge) | 0.008 | 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".