Large-Scale Qualitative Research with AI
The entities documented are the organisations that act in it: farms, processors, distributors, retailers, restaurants; and, at meso level, the actors that shape their environment, such as municipalities, government programmes, banks, NGOs and universities. This paper provides the technical reference for how the resulting data Corpus was built and managed to enable AI-augmented analysis. In its first phase (2023-2026) the pipeline produced 686 documented cases from 31 countries: some 1,430 hours of recordings, about 450,000 speech turns, and 12.6 million words of transcript.
- ▪The entities documented are the organisations that act in it: farms, processors, distributors, retailers, restaurants; and, at meso level, the actors that shape their environment, such as municipalities, government programmes, banks, NGOs a
- ▪This paper provides the technical reference for how the resulting data Corpus was built and managed to enable AI-augmented analysis.
- ▪In its first phase (2023-2026) the pipeline produced 686 documented cases from 31 countries: some 1,430 hours of recordings, about 450,000 speech turns, and 12.6 million words of transcript.
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Story provenance
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Record
| Original publisher | arXiv.org |
| Canonical URL | https://arxiv.org/abs/2608.29751 |
| Publication time | Mon, 14 Sep 2026 18:48:14 +0000 |
| Retrieval time | 2026-09-14T18:56:51.352Z |
| Last seen | 2026-09-14T18:56:51.352Z |
| Headline source | Publisher (no WeSearch rewrite) |
| Excerpt source | publisher body |
| Excerpt method | First ~120 words (~800 chars) of extracted publisher body, fair-use limited. |
| Summary | WeSearch · cerebras-chat (WeSearch summarizer) |
| Summary source text | contentText |
| Citation coverage | Summary is a WeSearch-generated derivative; primary citation is the original publisher URL. |
| Cluster | odeUBJrwQnvd · 1 stories |
| Cluster logic | Grouped by semantic title/content similarity across sources within a rolling window. Same-publisher template collisions are excluded from coverage comparison. |
| Ranking reason | Story pages are not engagement-ranked. Hub feeds use recency, with optional source-diversified chronological ordering (cap consecutive stories per source). No personalized ranking. |
| Publisher visit | Yes — open original |
| Substitutes article? | No — link-out required for full text |
Rights status (four layers)
WeSearch handling by dimension
| Indexing | May the item be indexed (stored, ranked, made findable)? | Allowed |
| Snippet | May a short excerpt of the publisher's text be shown? | Allowed |
| AI summary | May WeSearch generate its own short summary of the article? | Limited |
| Retrieval / RAG | May the content be exposed for third-party retrieval-augmented generation? | Not asserted |
| Model training | May the content be used to train AI models? | Not asserted |
| Commercial reuse | May the content be reused commercially? | Not permitted |
Basis: Derived from the published RSS/Atom feed. Contact: [email protected]. Reviewed: 2026-07-24.
Opening excerpt (first ~120 words) tap to expand
Computer Science > Computers and Society arXiv:2608.29751 (cs) [Submitted on 30 Aug 2026] Title:Large-Scale Qualitative Research with AI: Infrastructure, Management and Operation of the Socioscope Data Pipeline Authors:Saadi Lahlou (1 and 2), Juan Pablo Caicedo (1), Shriya Sekhsaria (1), Valentine Fournand (1), Paulius Yamin (1), Helga Nowotny (3) ((1) Paris Institute for Advanced Study, (2) London School of Economics and Political Science London, (3) Complexity Science Hub Vienna) View a PDF of the paper titled Large-Scale Qualitative Research with AI: Infrastructure, Management and Operation of the Socioscope Data Pipeline, by Saadi Lahlou (1 and 2) and 7 other authors View PDF Abstract:The Socioscope project is a pioneering effort in Large-Scale Qualitative Research (LSQR) collecting…
Excerpt limited to ~120 words for fair-use compliance. The full article is at arXiv.org.
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