Bad Mexicans: Race, Empire, and Revolution in the Borderlands
Kelly Lytle Hernández
Longlisted for the 2022 National Book Award for Nonfiction “Rebel historian” Kelly Lytle Hernández reframes our understanding of U.S. history in this groundbreaking narrative of revolution in the borderlands. Bad Mexicans tells the dramatic story of the magonistas, the migrant rebels who sparked the 1910 Mexican Revolution from the United States. Led by a brilliant but ill-tempered radical named Ricardo Flores Magón, the magonistas were a motley band of journalists, miners, migrant workers, and more, who organized thousands of Mexican workers—and American dissidents—to their cause. Determined to oust Mexico’s dictator, Porfirio Díaz, who encouraged the plunder of his country by U.S. imperialists such as Guggenheim and Rockefeller, the rebels had to outrun and outsmart the swarm of U. S. authorities vested in protecting the Diaz regime. The U.S.
Award History
1 wins · 5 total
| Award | Year | Result | Category / Notes |
|---|---|---|---|
| Bancroft PrizeMajor | 2023 | Winner | Official source |
| National Book Critics Circle Award for NonfictionMajor | 2022 | Finalist | Secondary source |
| Cundill History PrizeMajor | 2022 | Longlist | Secondary source |
| Mark Lynton History Prize | 2023 | Finalist | Secondary source |
| PEN/John Kenneth Galbraith Award for Nonfiction | 2023 | Finalist | Secondary source |
Experimental book profileGenerated by GPT-5.4 nano · may contain inaccuracies
This is an unverified interpretation of the catalog description, offered as an opt-in discovery experiment—not as bibliographic fact.
01 Central figures
- Ricardo Flores Magón86%
02 Central places
None extracted with sufficient confidence.
These suggestions did not meet the normal display threshold and are more likely to be wrong.
- the United States70%
- Mexico55%
03 Suggested argument
No argument inferred with sufficient confidence.
04 Reading orientation
35 / 100 academicSerious trade
An estimate of intended readership and scholarly apparatus—not quality or importance.
Confidence percentages are the model's own estimates. Profile confidence: 62%.