```json { "action": "final", "content": "## Column Classification\n\nBased on the metadata inspection of all 44 columns in `polling_on_ai_usage_jul_2026`, here is the complete classification:\n\n### **Identifiers** (2 columns)\n- `question_number` (BIGINT) — unique identifier for each survey question (17 distinct values, range 1-15)\n- `_batch_id` (VARCHAR) — ingestion batch identifier\n\n### **Categorical Dimensions** (4 columns)\nThese describe what was asked and answered:\n- `question_verbatim` (VARCHAR) — full text of survey questions (~15 distinct)\n- `response_verbatim` (VARCHAR) — full text of response options (~56 distinct)\n- `Question` (VARCHAR) — standardized question categories (~16 distinct, e.g., \"AI Services Used\", \"Work vs Personal\")\n- `Response` (VARCHAR) — standardized response categories (~64 distinct, e.g., \"1 day\", \"Yes\", \"Significant edits or changes\")\n\n### **Numeric Metrics** (34 columns)\nAll stored as VARCHAR containing percentage values (e.g., \"5.23%\"). These represent polling response rates broken down by demographic and occupational segments:\n\n**Overall:**\n- `Overall` — aggregate response rate across all respondents\n\n**Demographics (27 columns):**\n- **Gender (2):** `Gender: Male`, `Gender: Female`\n- **Age (4):** `Age: 18–29`, `Age: 30–44`, `Age: 45–59`, `Age: 60+`\n- **Income (5):** `Income: Under $25K`, `Income: $25K–$50K`, `Income: $50K–$75K`, `Income: $75K–$100K`, `Income: $100K+`\n- **Education (4):** `Education: Less than HS`, `Education: High school`, `Education: Some college`, `Education: Bachelor+`\n- **Employment (2):** `Employment: Full-time`, `Employment: Part-time`\n- **Race (4):** `Race: White, Non-Hispanic`, `Race: Black, Non-Hispanic`, `Race: Hispanic`, `Race: Other/2+, Non-Hispanic`\n- **Region (4):** `Region: Northeast`, `Region: Midwest`, `Region: South`, `Region: West`\n- **Occupation (9):** `Occupation: Software & IT`, `Occupation: Managers`, `Occupation: Business, finance & legal`, `Occupation: Education & social services`, `Occupation: Engineering & science`, `Occupation: Healthcare`, `Occupation: Sales, office/admin & creative`, `Occupation: Manual & in-person services`, `Occupation: Other (please specify)`\n\n### **Dates/Times** (1 column)\n- `_ingestion_timestamp` (TIMESTAMP) — when the data was loaded (single value: 2026-08-07 21:29:32)\n\n### **System Metadata** (3 columns)\n- `_batch_id` (VARCHAR) — already listed under identifiers\n- `_source_file` (VARCHAR) — source CSV path\n- `_source_system` (VARCHAR) — ingestion system (\"api\")\n\n### **Key Observations**\n\n1. **No native numeric types:** All metrics are stored as VARCHAR percentages rather than DECIMAL/DOUBLE, requiring `CAST(REPLACE(column, '%', '') AS DECIMAL)` for mathematical operations\n\n2. **No temporal dimension for analysis:** The single timestamp is a system field marking ingestion time, not a survey date or response period — this dataset cannot support time-series analysis of polling trends\n\n3. **Cross-tabulated structure:** Each row represents one question-response combination with breakdowns across 34 demographic/occupational segments, making this a pre-aggregated survey crosstab rather than raw respondent-level data" } ```