Link metadata – quality of evidence

Chapters▼

Chapters

Home Articles Glossary Working Papers Causal mapping – overview Task 1 – Gathering causal mapping data Task 2 – Causal coding – minimalist style Task 2 & 3 Key ideas and conventions Task 3 – Answering questions – General Task 3 – Answering questions – Individual questions Causal mapping in evaluation Causal Mapping as QDA Causal Map app and alternatives Deductive coding with AI Inductive coding with AI AI in qualitative social science Qualia Case studies AI and the wider world Finally Causal Map App

/

Pages▼

Pages in this chapter

Task 2 – Introduction Our approach is minimalist – we code only bare causation Our approach clearly distinguishes evidence from facts and does not automatically warrant causal inferences Our approach is minimalist – factors are not variables 1a A minimalist approach to coding helps capture what people actually say 1b A minimalist approach to coding makes aggregation easier 1c A minimalist approach to coding does not code absences Our approach is minimalist – we do not code the strength of a link In a causal mapping dataset there is no need for a special table of factors Factor labels – a creative challenge Factor label tags – coding factor metadata within its label Factor labels – semi-quantitative formulations can help Factor labels – do not over-generalise Coding with and using link metadata Link metadata – Sentiment Link metadata – Time reference Link metadata – quality of evidence Research on the ability of LLMs to detect causal claims

/

Tags▼

Tags

Case Studies

AI-assisted causal mapping. Uncovering causal pathways with INTRAC
Case study – our 'seamless stories' workflow in practice
Case study – Qualia asks about USA problems, again
DUOC UC. Evaluating Gender Equity in STEM with AI-Driven Interviews
Strengthening OH with causal mapping
Tree Aid - Empowering Communities Through Forest Management in Burkina Faso
Using AI to facilitate feedback on the learning experiences of doctoral students

Papers and Drafts

A formalisation of causal mapping
A simple measure of the goodness of fit of a causal theory to a text corpus
Assessing change in (cognitive models of) systems over time
Causal mapping as causal QDA
Combining opposites, sentiment
Despite-claims
Lonely in London
Magnetisation
Minimalist coding for causal mapping
Our paper on an inductive workflow to gather and analyse evidence at scale.

{'Date': '11/12/2025'}

AI-assisted causal mapping. Uncovering causal pathways with INTRAC

{'Date': '27/02/2025'}

Case study – Qualia asks about USA problems, again

🌻 Link metadata – quality of evidence

24 Oct 2025

Backlinks

Coding with and using link metadata