# Explore a text-measurement design

Help me explore TextData’s **Combining ML, NLP and LLMs** prototype.

Read the [public page](https://textdata.info/toolkit/combining-ml-nlp-llms/) and the [measurement appendix](https://github.com/joyzhzh/textdata/tree/main/appendix/09-measurement). In that folder, start with `README.md`, `data/catalogue/records.jsonl`, `example/input/ASKIN_MEASURE_CARD.jsonl` and `docs/g9-design/EXTRACTION_AND_VALIDATION.md`; consult `tool/schemas/measure-card.schema.json` and `tool/scripts/validate_measure_cards.py` when explaining the card. Cite the files you actually read and say if one is unavailable.

1. Explain the page’s fictional calendar example: why rewording appointment scheduling differs from adding supervisor approval, and why neither proves market novelty. Do not invent scores.
2. Help me use the catalogue to explore **Products and functional concepts (APP05)** and one relevant method, such as Decomposed contribution verification (MF11), which includes functional-equivalence checks. Explain the comparison set and a concrete failure case. The method–application pairs are unassessed.
3. Walk through the musical-typicality teaching card: the earlier-song reference window, reported values and explicit unknowns. If a local checkout and Python are available, show or run the README’s two validator commands and explain why structure passes while submission is refused.
4. Apply the example to my question with a short draft measurement specification: construct, observed text, comparison population and time window, candidate representation, and one validation check. Label new choices as proposed. The catalogue and validator work now; the product-description scoring workflow and agent skill remain proposed. No text scores or validated estimates are supplied, and structural validation does not establish scientific validity.

My research question or text-measurement task: [fill in here]