Michael Rowley's kanji picto graphix : dragon book : essential kanji mnemonics : blood・fire・spirit
Bibliographic record
Abstract
Kanji come to life with over 250 graphically illustrated mnemonics for learning essential Japanese characters. Colorful pages are filled with horses and tigers, palaces and pulpits, kings and lunatics, samurai and wizards. A world of soldiers, swords, spies, demons, fire, smoke, gushing blood, and dragons with claws and fangs. While the format looks and feels more like a colorful story book than a textbook, KanjiPictoGraphix Dragon Book taps powerful learning methods derived from Rowley's career as an educational therapist and professor of information design. The book begins with an 'Elements' chart of the building blocks of Japanese written language. Pages are organized into clusters of characters with common elements and meanings. The result is a semantic, meaningful narrative that makes learning the complex written forms easy to understand and remember. The hundreds of visual mnemonics draw upon a combination of visuals with genuine etymological roots along with contemporary visual interpretations to help you learn to read Japanese kanji quickly and joyfully. Kanji are over 2,000 years old, so you will see some non-PC imagery that reflects ancient ideas about religion, women, men, children, animals, and old people. Rather than whitewash this, the book illustrates the meanings and ideas of an ancient, beautiful, and, at times, provocative language. Michael Rowley is the founder and creative director at VizCab.com, a family-run print and mobile media design and branding business in Silicon Valley, California. He is the author of KanjiPictoGraphix for iPhone and iPad. His Twitter feed @KanjPicto distills and dissects the meanings of Japanese kanji vocabulary words. Early in his career Michael worked as an educational therapist at the Dannen School at La Canada, taught English at Chaminade University Tokyo, digital imaging at The American Film Institute in Hollywood, and information design at Art Center Pasadena. He lives with his sweetheart, Kiki, and their four dogs and is a volunteer at ProjectRescueMe.com, an organization to rescue animals and end petlessness.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.066 | 0.023 |
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".