action namsor_us_race_ethnicity_zip5 { label: "[USES 10 UNITS PER NAME] Infer a US resident's likely race/ethnicity according to US Census taxonomy, using (optional) ZIP5 code info. Output is W_NL (white, non latino), HL (hispano latino), A (asian, non latino), B_NL (black, non latino). Optionally add header X-OPTION-USRACEETHNICITY-TAXONOMY: USRACEETHNICITY-6CLASSES for two additional classes, AI_AN (American Indian or Alaskan Native) and PI (Pacific Islander)." provider: namsor method: GET path: "/api2/json/usRaceEthnicityZIP5/{firstName}/{lastName}/{zip5Code}" encoding: json input: { type: "object" properties: { firstName: { type: "string" } lastName: { type: "string" } zip5Code: { type: "string" } } required: ["firstName", "lastName", "zip5Code"] additionalProperties: false } output: { type: "object" description: "Represents the output of inferring the LIKELY US 'race/ethnicity' from a personal name, given US country of residence and (optionally) a ZIP5 code." properties: { firstName: { type: "string" description: "The first name (also known as given name)" } id: { type: "string" } lastName: { type: "string" description: "The last name (also known as family name, or surname)" } probabilityAltCalibrated: { type: "number" format: "double" description: "The calibrated probability for raceEthnicity OR raceEthnicityAlt to have been guessed correctly. -1 = still calibrating. " } probabilityCalibrated: { type: "number" format: "double" description: "The calibrated probability for raceEthnicity to have been guessed correctly. -1 = still calibrating. " } raceEthnicitiesTop: { type: "array" description: "List 'race'/ethnicities" items: { type: "string" description: "List 'race'/ethnicities" } } raceEthnicity: { type: "string" description: "Most likely US 'race'/ethnicity" enum: ["W_NL", "HL", "A", "B_NL", "AI_AN", "PI"] } raceEthnicityAlt: { type: "string" description: "Second most likely US 'race'/ethnicity" enum: ["W_NL", "HL", "A", "B_NL", "AI_AN", "PI"] } score: { type: "number" format: "double" description: "Higher score is better, but score is not normalized. Use calibratedProbability if available. " } script: { type: "string" } } } }