Bibliographic record
Abstract
Creating a world of colors In 2019, Victor Casale got a cryptic email from Crayola asking him to work with the company on a secret project. The cosmetic chemist’s mind immediately jumped to that single pinkish crayon that has been the primary skin-color choice for kids for decades. Crayola soon confirmed it was recruiting Casale for its Colors of the World project , and he jumped at the chance to help create a whole new skin-color palette of crayons. “I said, ‘Are you kidding me? I was born to do this,’ ” he says. Casale has been thinking about skin color for decades. In the 1980s, he was a chemistry undergraduate at the University of Toronto when his future brother-in-law asked him to help him make lipstick. That effort eventually turned into a successful career as a color chemist and entrepreneur with MAC Cosmetics, Cover FX Skincare, and a new company,
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.017 | 0.006 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.001 | 0.013 |
| Research integrity | 0.002 | 0.008 |
| Insufficient payload (model declined to judge) | 0.099 | 0.016 |
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".