Bibliographic record
Abstract
LungsThrough my painted woodcut Lungs, on the cover of this issue, I explore the intersection between fragility and resilience, the biological and the artificial, private and public, decay and resuscitation, and the body and architecture. I am particularly interested in how our sense of embodiment and identity become profoundly affected by illness, diagnosis, and recovery. My artwork is informed by my experience of surviving cardiac arrest and having long QT syndrome, a condition that affects the heart’s electrical system. I rely on an implantable cardioverter-defibrillator to regulate heart rhythms and prevent sudden cardiac arrest. By fusing traditional print media with new technologies, I use my work to examine the human body as seen through the lens of medicine and science. I employ computer-operated carving machines to complete the labor of carving woodcuts. Like Lungs, my painted woodcuts interrogate what it means to be dependent upon a mechanical device for survival, to inhabit a cyborg-like existence as part human/part machine. These questions not only are personally relevant but can also be applied to the current transformation of human existence due to our increasing reliance upon many different types of technologies.
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 | 0.001 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.006 | 0.008 |
| Insufficient payload (model declined to judge) | 0.141 | 0.044 |
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".