Social Science Text Classification FAQs

Question 1

Can this skill handle multi-label classification?

Accepted Answer

Yes, it includes guidance on explicitly instructing the model to allow multi-label assignments and how to specify that in the prompt and output format.

Question 2

What is the best learning regime for text classification?

Accepted Answer

The ideal regime depends on your data: Zero-shot is best for rapid prototyping with large models, while fine-tuning smaller models or using instruction-tuning is preferred for complex tasks or cost-effective scaling.

Question 3

Why does this skill recommend open-weight models?

Accepted Answer

Open-weight models (like Llama 3 or Mistral) are preferred for research because they produce lower and more predictable variance across runs compared to proprietary APIs, which can change without notice.

Question 4

How many human-coded examples are needed for validation?

Accepted Answer

It is recommended to hand-code 50-100 responses as ground truth to benchmark performance and calculate precision, recall, and F1 scores.

Question 5

How do I prevent 'preamble' text in LLM classification outputs?

Accepted Answer

The skill provides instructions for the system prompt to enforce exact output formats, such as code labels only or JSON formatting, to ensure the model doesn't return conversational filler.

Social Science Text Classification

Key Features

Use Cases

Social Science Text Classification

Key Features

Use Cases