Bibliographic record
Abstract
When Ross Kind decided to tell the story of Michelangelo painting the Sistine Chapel, he didn’t start with the paint colors or brushes; he started with politics, gossip, power and intrigue. When he told the story of Brunelleschi’s dome for the Basilica di Santa Maria del Fiore in Florence, he started with competition and rivalry. Is this how we should tell the story or art? Is one painting or one building so complex, that he needs hundreds of pages to prepare the audience? Ross King thinks so and we’re going to find out why. Ross King is the bestselling author of six books on Italian, French and Canadian art and history. He has also published two historical novels, Domino (1995) and Ex-Libris (1998), and edited a collection of Leonardo da Vinci’s fables, jokes and riddles. Translated into more than a dozen languages, his books have been nominated for a National Book Critics’ Circle Award, the Charles Taylor Prize, and the National Award for Arts Writing. He has won both the Governor General’s Award in Canada (for The Judgment of Paris) and the Book Sense Non-Fiction Book of the Year in the United States (for Brunelleschi’s Dome). His latest book, Leonardo and The Last Supper, has been described as ‘gripping’ (New York Times), ‘fascinating’ (Financial Times), ‘engaging’ (The Guardian), ‘enthralling’ (Daily Mail), ‘absorbing’ (Kirkus), ‘engrossing’ (Booklist), and ‘extraordinary’ (Irish Times). Leonardo and The Last Supper was awarded the 2012 Governor General’s Award for Non-Fiction. Ross King’s website can be found here.
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.014 | 0.020 |
| Scholarly communication | 0.016 | 0.015 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.005 | 0.009 |
| Insufficient payload (model declined to judge) | 0.027 | 0.014 |
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".