# Prompt 5 — Draw the human validation sample

## Context
The AI-coded dataset of the full 946-article corpus is `dataset_ai_coded.xlsx` (sheet `instances`). We now need a sample of rows for human coders to code blind, so that human–AI and human–human agreement can be measured.

## Task
1. Draw a validation sample of **N rows** (workshop default 10, so that every participant can code the whole sample in twenty minutes; for a real project 10–20% of the dataset, and at least 50 rows). Stratify by `coder_confidence` so that both high- and lower-confidence rows are represented (about 60% confidence 3, 30% confidence 2 or 1), and include one or two `relevance = 0` rows so that the relevance screen is tested too. Use a fixed random seed and record it.
2. Write **`human_sample_blank.xlsx`**: the sampled rows with `instance_id`, `doc_id`, `article_date`, `article_long_title`, `article_url` and `article_cleaned_body` kept, and every coded field (`relevance`, `enterprise_name`, `sector`, `event_type`, `leader_visit`, `province`, `evidence_text`, `coder_confidence`, `notes`) emptied. Add a `coder_name` column first. Add a README sheet with coding instructions and a pointer to the codebook. Add Excel data-validation dropdowns from the codebook's `Dropdown_Values` sheet if you can.
3. Write **`human_sample_key.xlsx`**: the same rows with the AI codes retained. This is the instructor's key and is not distributed to coders.
4. Report the sample's composition (confidence strata, years, sectors) against the full dataset so I can see it is not skewed.

## Output
The two workbooks plus a five-line summary: N, seed, strata counts, and anything about the sample you would change.