// Landing page: what CCR is, who runs the platform, and the doors in - // dashboard, guide, product/architecture. First-time visitors land here // (localStorage flag); the header "About" link brings anyone back. const STEPS = [ { n: "1", title: "Upload your texts", body: "A CSV or Excel file with one text per row - tweets, essays, open-ended survey answers, transcripts.", }, { n: "2", title: "Pick a validated scale", body: "Choose from the construct library (90+ published psychological scales) or paste your own questionnaire items.", }, { n: "3", title: "Get scores you can defend", body: "Every text is scored against every item, with distributions, per-item loadings, warnings, and a downloadable script that reproduces the numbers on any machine.", }, ]; export default function WelcomePage({ onEnter }) { return (
Contextualized Construct Representation (CCR) offers a theory-driven way to measure psychological themes in text without relying on simple word counts. CCR uses validated questionnaire items (or prototypical statements) designed to assess psychological constructs such as individualism, moral values, depression, religiosity, or personality traits. A language model then compares the meaning of a passage with the meaning of each input sentence and produces a score indicating how strongly the passage reflects the construct of interest. CCR is free, open-source, publicly available, multilingual, adaptable to new language models, and highly flexible.
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The embedding models run on this server - no text is ever sent to third-party AI APIs. Every run records the exact model version, scale wording, and package versions, so results are auditable and reproducible outside the platform.
This interface is designed, built, and maintained by the{" "} Culture & Morality Lab {" "} (PI:{" "} Mohammad Atari ) at the University of Massachusetts Amherst. The CCR pipeline is built based on Atari et al. (2023), extended and validated by Chen et al. (2024), and features PsyEmbedding language models, which are fine-tuned open-source models for psychological text analysis (Atari et al., 2026). For suggestions, questions, bugs, and ideas, reach out to the maintainer at devaanand@umass.edu.
This is an open-source software: use at your own risk. This open-source software is provided "as is," without warranties or guarantees of accuracy, reliability, security, fitness for a particular purpose, or continued support. Users are responsible for validating outputs, protecting their data, and determining whether the software is appropriate for their intended use. The authors and contributors are not liable for losses or damages resulting from its use.