Bibliographic record
Abstract
The central theme of the artwork on this issue’s cover is the tension between educational objects and aesthetics. This artwork portrays a found anatomical model, which contains substantial liberties in the generalization of structures, saturation of color, and a glossy façade. It is an object that, nonetheless, is primarily meant for teaching purposes. This print acts to emphasize the contrast concerning the reality of our internal matter and the fetishization of the representative object. This print was created using a color separation process that has been bound together with wax. After digitally photographing the model, I split the image into the four colors that are used for industrial commercial printing: cyan, magenta, yellow, and black—commonly referred to as CMYK. I then printed these colors over one another using wax as the toner binding agent between each of the four layers. The resulting image has intense color and depth because of the layering of toner and wax. This print recalls the past and present of anatomy education by combining modern commercial printing practices with the historical use of wax as anatomical models. Pixels In at once reveals the inner anatomy while bringing attention to the artifice of the object. The hypersaturated colors and the gradient of enlarged pixels that hang over the object further the disconnection between model and reality. The more liberties taken to present a marketable model, the more it is distanced from a tool of education. This print is meant to remind the viewer that our bodies are not made of pixels, but of real matter that is not always beautiful, but is always wondrous. To see more of Darian’s work, please visit her Web site, www.dariangoldinstahl.com.Pixels InD.G. Stahl is a PhD student in fine art humanities, Concordia University, Montreal, Quebec, Canada; [email protected]
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.389 | 0.143 |
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".