
The Confession Written in a Language He Couldn't Read
In 1970s America, a Spanish-speaking laborer signed an English confession he never understood, sending him to prison for a murder he didn't commit. This episode traces how a translation gap became a legal weapon and how the document was finally exposed.
Listen in the Fylom app.
The nineteen seventy Negrón ruling established that defendants are effectively absent if they cannot understand the court's language.
Twelve out of twenty-nine Latinx exonerees who falsely confessed lacked basic English proficiency.
Translation drift occurs when police transcripts remove linguistic hedges like maybe or I think from suspect statements.
Early Miranda cards contained significant errors and omitted the critical right to stop questioning.
Courts now use a totality of circumstances test to determine if a suspect truly understood their legal waivers.
Street-level English proficiency differs significantly from the ability to negotiate complex legal concepts during interrogations.
- 01Intro1 min
- 02The Silence of the Court2 min
- 03The Mechanics of the Interrogation Room3 min
- 04Translation Drift and the Paper Trail3 min
- 05The Right to Confront the Translator2 min
- 06Outro1 min
- U.S. ex rel. Negrón v. New York - Language Policy Web Site
- Totality of Circumstances and Translating the Miranda Warnings
- Judicial Reasoning on Miranda Waivers by Speakers with ...
- UNITED STATES v. MARTINEZ GAYTAN (2000)
- Estrategias lingüísticas en el interrogatorio judicial español: una aproximación pragmalingüística
- The Routledge handbook of forensic linguistics 2020020527, 9780367137847, 9780429030581 - DOKUMEN.PUB
- Barriers to Justice: How Spanish-speaking Suspects Are At Risk ...
- United States v. Alvarado-Palacio, No. 17-51030 (5th Cir. 2020)
- [PDF] U.C.L.A. Law Review
- ERIC - EJ1365329 - Language and Culture as Sources of ...
- Resolution 110 - Miranda
Fylom generates episodes like this on any topic you're curious about.
Fylom episodes are researched, written, and voiced by AI. Automated checks help catch inaccuracies, but episodes aren't reviewed by a human and AI can still get things wrong. Treat them as a starting point, not a source of record — more in our accuracy disclaimer.