Bibliographic record
Abstract
The artist's statement: I began my journey to design through a passion and background in drawing. My passion for drawing led me to discover hand-lettering which ignited my love for typography. The push to use technology in my art process, and my passion for letters created an easy segue to explore graphic design. When I first began designing, I found great joy and ease being able to apply my traditional art skills/background in a more tangible way, and reaching a wider audience. I do not view design as something purely functional, and I feel there are opportunities for fun, creativity, and true art in most projects.I find that every design is like a puzzle, with each element having its perfect place and purpose in the final work. Because I find my work to be so personal, I enjoy making my work more recognizable by leaning into humor and unique methods/styles that interest me. Keeping my work interesting for myself is important because this greater passion produces more ideas and greater results. In my work, I try to do as much as possible by hand, whether this is illustration, hand-drawn typography, or manipulating physical materials. Along with this I put great intention and emphasis into the typography and colors of my work, aiming for visual interest and cohesion throughout the style and content.
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) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.826 | 0.605 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".