Lab Rats and Book Bodies: Creating Intersubjectivity for Patients and Practitioners
Bibliographic record
Abstract
The labyrinthine basement of a biomedical research laboratory is an unlikely place to find an artist. In 2019, I pursued an artist residency at the McGill University Fertility Research Laboratory to better understand the mechanisms of ovulation that seemed to be failing my sister’s attempts at conception and motherhood, in addition to the other ways her body continued to oppose her will. My goal was to procure source material for a person visualizing infertility while also living with chronic illness. What I had not expected was the expansion of this arts-based research to envelop not only the scientists working in this lab, but the lab mice also residing in this space. The final format of the artwork as a handmade book promotes an intersubjective experience of illness and health, as readers use their bodies to engage with the voices and unique materials held within this multi-sensory medium. The outcomes of this residency, along with numerous other artworks featured in my recently published book, Embodied Books: Experiencing the Health Humanities Through Artists’ Books (Figure 1), make ‘sense-able’ to healthcare workers, learners, and the general public the extraordinarily complicated issues of ethics and agency when it comes to medical care.
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 | 0.006 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.020 |
| Scholarly communication | 0.011 | 0.011 |
| Open science | 0.002 | 0.012 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.028 | 0.008 |
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".