Bibliographic record
Abstract
Guy Kawasaki is the chief evangelist of Canva, an online graphic design tool. Formerly, he was an adviser to the Motorola business unit of Google and chief evangelist of Apple. His in-depth knowledge of the high-tech industry combined with his years of management experience enables him to address a wide range of audiences. His particular strength is the ability to quickly understand diverse industries and incorporate his pre-existing knowledge into a highly relevant and customized speech. He is also the author of "Enchantment: The Art of Changing Hearts, Minds, and Actions," "APE," "What the Plus!" and ten other books. His two latest books are "The Art of the Start 2.0" and "The Art of Social Media. Guy routinely gets rave reviews from clients including trade associations, packaged goods companies, service providers, insurance companies, educational institutions, and technology companies. He has spoken for organizations including Google, Nike, Audi, TEDx, Wal-Mart, Sprint, Hewlett-Packard, IBM, Saturn, Stanford University, TIE, Calgary Flames, The Body Shop, MIT, Forbes and Aveda.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.005 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.017 | 0.006 |
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".