Bibliographic record
Abstract
My research-creation dissertation investigates the ways we are all enmeshed in the world, as well as how we can productively imagine ways to live together. I approach this research through thinking-with, a concept proposed by Donna Haraway. Throughout this text and my dissertation exhibition I think-with many partners including microbial life, Lake Ontario, mud, the field of bio art, and other researchers. My dissertation exhibition provides an opportunity for viewers to encounter microbial life. In the creation of this body of work, I have collected mud from Lake Ontario and placed it, along with nutrients that encourage microbial growth, in clear sculptural prisms. When exposed to light, the microorganisms already present begin to flourish, becoming visible in the form of vibrant marbling. In this text I discuss how my thinking partners have shaped my research, which takes the form of writing, artworks, and workshops. I go on to engage geographic borders, such as those between land and water, or between Canada and the United States of America; biologic borders that we use to distinguish one organism from another; and the borders that are drawn around disciplines or types of knowledge. I argue that working across divides is essential to forging a more sustainable future. I also explore different forms of collaboration that have become critical in the way I conduct research: collaborating with scientists, with non-humans, and with the public. Throughout the text I argue for the value of thinking-with, working across borders, and forging meaningful collaborations. I argue that working together is an important strategy for deeper understanding, as well as responsible action in the face of ecological crisis.
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 | 0.011 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.019 | 0.067 |
| Scholarly communication | 0.026 | 0.018 |
| Open science | 0.002 | 0.028 |
| Research integrity | 0.003 | 0.008 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".