Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Endemic 3Endemic 3, on the cover of this issue, is part of “Vicarious Atonement,” a series of oil-on-canvas portraits exploring our local and global reaction to the COVID-19 pandemic. This work depicts the sometimes-ineffective efforts by scientists and public health advisors to show the public data in a meaningful way that connects statistics to personal and collective responsibility. The series takes up questions of empathy, relationality, and accountability. Work from this ongoing painting series uses a bank of 100 years of epidemic data fed through an AI program that blends data with selected AI-generated faces. The outputs are then further curated by the artist based on esthetic appeal, lighting, composition, and a number of other formal choices before these source images are reproduced as hand-painted artworks. Painting enters the images into the canon of portraiture, which carries some authority and history and lends some weight to the characters as lives documented. The series title “Vicarious Atonement” is born from an emphasis on the vicarious experience of putting an AI-generated “face” to the data while playing with the Christian notion of substitutionary atonement, the idea that someone died in our place. The work offers up a face for meaningful, empathetic engagement with epidemic statistics, but the people depicted in these portraits, like the man in Endemic 3, do not exist. Though perhaps successful, or even useful, the entire gesture is empty. Thus, the project lays bare our limitations when it comes to humanizing statistics and empathy.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.004 |
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 it