Artist’s Statement: Silent Blooms, Silent Struggles
Bibliographic record
Abstract
The piece Silent Blooms, Silent Struggles, on the cover of this issue, was inspired by my experience with my collaborators on this piece (Maggie Wang and Kevin Li) in the Patient Immersion Experience (PIE) program at the University of Alberta Medical School. PIE is an ongoing learning experience where patients battling various illnesses mentor medical students regarding their achievements and struggles. For many of these patients, their conditions do not manifest outwardly, which has led to stigmatization by society, including the dismissal and invalidation of their illnesses. This artwork aims to combat these biases by raising awareness and by fostering an understanding of their lived experiences. The artwork’s subject, painted on Procreate with muted tones in a classical art style, embodies the calm demeanor that patients often maintain while facing internal struggles. Her vacant gaze and ambiguous smile mask the turmoil that lies within, although its mental burden reveals cracks in her facade. She is juxtaposed by a vibrant explosion of flora reminiscent of neural tissue, drawn in colored pencil and digitally collaged onto the piece. The neuronal extensions behind her, resembling a chaotic and overgrown root system, represent the beautiful yet turbulent thoughts beneath the surface.Silent Blooms, Silent StrugglesThis piece seeks to educate both medical professionals and the community about the importance of recognizing and addressing the stigmas associated with invisible illnesses. We hope it reminds us all of the need for empathy and understanding when interacting with those whose struggles may not be visible but are nonetheless deeply impactful.
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 | 0.002 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.008 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.009 | 0.012 |
| Insufficient payload (model declined to judge) | 0.056 | 0.020 |
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".