Bibliographic record
Abstract
After a hiatus of many years, I came back to drawing in the middle of the COVID-19 pandemic. I was working as an academic emergency physician in downtown Toronto. As COVID-19 swept through our emergency department, we put everything we had to the test: our knowledge, our innovation, our resolve. We pulled on masks, goggles, and face shields and showed up to do what we had been trained to do.I Miss All Your Beautiful Faces, Portrait #4In my off hours, I started drawing portraits of my colleagues in full personal protective equipment, including I Miss All Your Beautiful Faces, Portrait #4, on the cover of this issue. Along with my colleagues, I was wrestling with being warrior and human, resolute and broken, okay and not okay. Drawing became an outlet for that tension. I Miss All Your Beautiful Faces, Portrait #4 This drawing was selected from my larger visual essay of the frontlines during the height of COVID-19 in 2020. It explores being “both/and”—what we are in our wholeness, a vulnerable and expressive wholeness, which is not always open to us in professional circles. My drawings are made with conte and paper. I have purposefully limited these drawings to black and white as both a gesture toward the narrative quality found in the graphic novel tradition and for the haunting quality the material evokes.
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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.192 | 0.075 |
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".