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Large-Scale Qualitative Research with AI: Infrastructure, Management and Operation of the Socioscope Data Pipeline

Saadi Lahlou, Juan Pablo Caicedo, Shriya Sekhsaria, Valentine Fournand, Paulius Yamin, Helga Nowotny

arXiv:2608.29751v1cs.CY

TL;DR

The paper addresses how qualitative research can combine breadth and depth when studying complex societal systems. It presents the Socioscope’s AI-assisted LSQR pipeline for building and managing comparable open-ended data, and reports a first-phase corpus of 686 cases from 31 countries. The authors conclude that scale depends on organisational coordination and a traceable pipeline as much as on technology.

  • Problem

    Research on complex societal systems remains constrained between broad but often generic data and deep case studies that are difficult to compare or generalise.

  • Method

    The Socioscope builds a purpose-designed LSQR corpus by collecting comparable open-ended data across hundreds of entities and using AI to support analysis and research coordination.

  • Results

    686 cases from 31 countries, 1,430 hours of recordings, and 12.6 million words of transcript were produced in under three years.

  • Takeaways & Limitations

    The paper argues that organisational coordination and a provenance-preserving pipeline are conditions for reproducible qualitative research at scale.

  • Takeaways & Limitations

    The released Corpus has gate-based quality control but lacks published rate-based error metrics, inter-rater agreement, and word-error rates.

Abstract

from arXiv · show

The Socioscope project is a pioneering effort in Large-Scale Qualitative Research (LSQR) collecting comparable, open-ended, multimedia field data on hundreds of cases and using AI to make the material analysable at scale. The domain studied is the food system. 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. It describes the data pipeline end to end: the systemic sampling frame; the transaction grid used to capture each initiative's relations within the food system; the social contract that rewards participating interviewees, aiming to sustain access; the operational chain from scouting to interviews, including their uploading, transcription, translation, quality control and curation; the provenance rules (originals are immutable, every transformation is logged); and the installation of equipment, personnel and processes, including ethics and GDPR compliance. 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. We report costs, metrics, lessons learned and limitations, so that other teams can reuse, adapt, and improve the Socioscope methodology.

1 Big data, deep data, AI, Large-Scale Qualitative Research and the Socioscope project

The Socioscope develops Large-Scale Qualitative Research by combining the breadth of comparable multi-case study with the depth of open-ended qualitative data, using AI and formal infrastructure. The paper documents this approach and reports its first-phase implementation in the food system.

  • Combining breadth and depth: LSQR combines large-scale comparison with open-ended, in-depth data collection across many cases using a common protocol and AI-assisted analysis.The Socioscope applies this approach to entities and relationships in the food system.
  • Analytical opportunities: At scale, the corpus supports classifications, typologies, ontologies, hypothesis testing across hundreds of cases, cross-level comparison, and discovery of variables not anticipated during collection.The open-ended corpus can also address questions that were not asked when it was collected.
  • AI and coordination: AI can support project coordination by recording decisions, definitions, exceptions, case states, and instrument changes, helping teams remain coherent as projects grow.The paper identifies coordination rather than storage or compute as a historical constraint on qualitative research.
  • Costs and infrastructure: Scaling qualitative research introduces higher collection and management costs, new participation and interpretation practices, and a need for formal research infrastructure.The paper presents the Socioscope’s operational experience as a basis for others taking the same path.
  • First-phase implementation: 686 cases from 31 countries were released in the Corpus during the first phase, while more than 700 initiatives had been documented across 37 countries.The released Corpus excludes cases still in final processing.
  • Building comparable data: The Socioscope builds comparability upstream by creating original qualitative data for systemic analysis rather than harmonising heterogeneous legacy studies afterward.Each case records initiatives and their relations with the wider system.

2 The Socioscope Data Pipeline

The Socioscope defines initiatives as its sampling units and builds cases that situate them within food-system relationships. Its pipeline combines diverse systemic sampling, transaction-grid interviews, AI extraction, and a participation-oriented social contract.

  • Sampling frame: The study samples food-system organisations and related meso-level actors to examine mechanisms moving the system toward greater sustainability.
  • Sampling frame: The initiative is the sampling unit, while the case is the digitised and archived material collected from that entity.
  • Sample selection: Because representative sampling of the interconnected food system is impossible, the project maximises diversity among typical, embedded, robust, and change-oriented initiatives.
  • Transaction grid: The transaction grid systematically records stakeholders, what the initiative gives and gets, and relationships from multiple perspectives.
  • Transaction grid: AI reconstructs transaction grids from interview recordings and other case materials, but extraction remains sensitive to models, prompts, strategy, and granularity.
  • Participation: A social contract treats participation as reciprocal rather than extractive, addressing research fatigue among busy data sources.

3 Data enrichment and release for analysis

Data enrichment is managed as a provenance-preserving cycle in which originals remain available while transformations, quality checks, access, and analysis-driven corrections are recorded. The Gate delivers controlled, reproducible retrievals and supports iterative enrichment.

  • Iterative curation: The pipeline cycles between collection, curation, analysis, identification of missing material, enrichment, and renewed analysis.
  • Provenance: Original recordings remain unchanged while transcripts, translations, tags, and extracted data are added as traceable layers linked back to source material.
  • Gate curation: The Gate pseudonymises data when required, generates metadata and audit records, flags quality issues, and commits numbered case versions.
  • Quality control: Source links help analysts recover linguistic nuance and detect errors such as misattributed speech turns, which remain possible despite improving tools.
  • Quality control: Quality control creates correction loops, so the pipeline combines a linear stream with upstream revisions and overarching access and data management.
  • Release for analysis: For analysis, the Gate provides scoped retrievals, immutable citable snapshots, logged exports, and controlled access, while analysis can trigger further enrichment.

4 The Socioscope installation: equipment, personnel and processes

The Socioscope installation combines field equipment, central and local personnel, software, websites, logistics, and governance processes. Its scale requires professional training, quality control, durable infrastructure, and substantial compliance work.

  • Infrastructure: The equipment layer supports field collection, storage and processing, curation, and fulfilment of the social contract.
  • Field operations: Interviewers use standardised audiovisual kits, with dispatch and readiness checks coordinated centrally or regionally.
  • Field operations: Field coordination combines project email, ordinary communication channels, a help desk, hotline, and shared case tracking.
  • Software infrastructure: The Gate authorises uploads, renames and provenance-stamps files, logs activity, and connects redundant cloud storage to a searchable database.
  • Web infrastructure: Separate internal and public websites manage project documents and processes, communicate the project, provide initiative visibility, and support search and connections.
  • Operational management: The former dashboard tracked leads and cases across geographies, while the Gate replaced it as the central operational system.
  • Training and quality control: Only 5 or 6 of 30 proof-of-concept interviewers were retained as reliable in teamwork and instruction-following, exposing collaborative fieldwork as a major organisational challenge.
  • Training and quality control: Multimedia LSQR therefore requires detailed training, serious quality control, and corresponding investments in time, cost, organisation, careers, and funding.

5 Data journey step by step

The pipeline moves each initiative from scouting and validation through contracting, field collection, upload, targeted debriefing, transcription, translation, and quality-controlled curation. Human judgement remains central where context, rapport, consent, or accountability matter, while AI handles repetitive processing and supports review.

  • Scouting and preparation: Initiatives are scouted, logged, web-described, and validated before suitable interviewers, permissions, dates, and equipment are coordinated.Rejected and unreachable initiatives remain recorded so later analyses can assess conversion, coverage, and bias.
  • Field collection: Interviews combine long open-ended audio-video conversations with standardized social-contract footage, site recordings, documents, website material, and interviewer debriefs.The central team uses debriefing both to check completeness and to provide external quality control.
  • Processing: After upload, the Gate checks files, applies naming and provenance metadata, and processes transcripts through transcription, translation, transliteration, and entity handling.Processing varies by language, with less common or mixed languages sometimes requiring different tools.
  • Quality control: Quality control runs throughout the pipeline because early errors cascade into later stages, including entity tagging and speaker attribution.Source audio remains essential for detecting misattributed speech turns and other transformation errors.
  • Publication and review: The recommended editing model is AI-assisted: tools perform captioning, subtitling, transcript-based editing, and other mechanical work, while humans shape and review the final representation.The paper identifies creative assembly, emphasis, tone, and final review as human responsibilities.

6 Variations and costs of the pipeline

The paper compares human-only, tech-maximal, and hybrid pipeline configurations, recommending the hybrid because automation reduces mechanical costs while humans retain judgement-dependent tasks. Cost figures are marginal per-case estimates and exclude the installation and shared infrastructure required to operate the project.

  • Configurations: The pipeline is evaluated in human-only, tech-maximal, and hybrid configurations, with the hybrid assigning each task to the approach it best suits.The authors explicitly frame the choice as a trade-off rather than assuming that maximum automation is cheapest overall.
  • Cost comparison: At 100–500 cases, the hybrid costs about a third less than the human-only version while retaining human control over quality, consent, and relationships.The saving is achieved without automating steps where judgement or accountability is required.
  • Automation: Automating interview and debrief transcription and diarisation cuts those two task costs by about 96%.The paper attributes the saving to administrative and mechanical work for which quality control detects no loss.
  • Human work: Human fieldwork remains the dominant marginal cost because it includes scouting, negotiation, planning, travel, filming, data management, and exchanges beyond recorded interview time.The recorded material itself occupies barely two of the interviewer’s four and a half budgeted person-days per case.
  • Cost boundary: The reported costs are marginal data-collection costs, not the full project budget, excluding fixed and shared costs such as technical infrastructure, supervision, legal support, and instrument design.For 686 cases, fieldwork involved about 3,100 contracted person-days versus 5,800–6,000 person-days for the whole data-collection operation, still excluding several central functions.

7 The Socioscope pipeline implementation: primary data and documentation

The pipeline produced a multilingual Corpus through a funnel from broad scouting to validated and completed cases, while maintaining release controls, provenance records, and a substantial documentation library. By July 2026, the analysable Corpus covered 686 cases in 31 countries alongside extensive recordings, transcripts, administrative records, and technical documentation.

  • 7.1 The Corpus: Conversion reached 67% in Colombia, but a June 2025 peak of 110 monthly closures exceeded the five-person team’s processing capacity and forced a slowdown.The median time from log entry to closure was two months, and 70% of dated cases closed within three months.
  • Quality and provenance: Corpus release is Gate-based: recordings carry quality flags, and cases cannot be released while issues such as corruption, inconsistent speaker attribution, or divergent entity spellings remain open.Independent review of each case added a separate completeness check covering about 39 points.
  • 7.1 The Corpus: 686 cases across 31 countries formed the latest analysable Corpus, with collection concentrated in ten primary countries and 121 cases distributed across 21 others.About 40 additional cases remained in the incoming pipeline awaiting completion requirements.
  • 7.1 The Corpus: Spanish accounted for 51% of speech turns, English 32%, French 12%, and Danish 3%, with a dozen further languages comprising the remainder.The language mix reflects the project’s geographically distributed sampling.
  • 7.1 The Corpus: Each case combines filmed settings, facility tours, long interviews, standardized short videos, supplementary material, debriefs, and provenance records.The July 2026 snapshot contained about 140 hours of video and 1,290 hours of audio, plus supplementary material.
  • 7.2 Internal documentation: The documentation library contained 1,432 unique documents and 93 transcribed recordings organized across fourteen categories covering governance, operations, and publication.Administration accounted for 678 documents, while training and field support accounted for 324.
  • 7.2 Internal documentation: The master case log held 54 country sheets and 3,247 dated lead rows, showing that document counts understate the administrative mass of standardized operations.The paper trail is comparatively thin for scientific protocols and thick where the operation manages people and money.

8 Lessons learned and limitations

The Socioscope’s large-scale collection succeeded through adaptation and coordination, but exposed operational bottlenecks, uneven language quality, and incomplete quality evidence. Its Corpus remains constrained by non-probability sampling, standardisation blind spots, translation uncertainty, and rapidly changing tools.

  • Lessons learned: 101 and 110 cases were closed in May and June 2025, overwhelming central processing capacity and forcing operations to slow.Transcriptions and debriefs fell behind, while budgetary limits risked being reached prematurely.
  • Lessons learned: Transcription and translation quality remained uneven across languages, requiring hybrid machine-human workflows and local linguistic expertise.isiXhosa required manual transcription, while several other languages produced context and idiom errors that native speakers corrected.
  • Limitations: The Corpus has no unresolved release flags, but published rate-based quality metrics are unavailable and procedures were not applied uniformly across the Corpus.Homogenising passes in May, June and July 2026 followed earlier corrections of many errors and missing data.
  • Limitations: The 197-case completeness review found 60% average checklist coverage, while external assessment still rests on architecture rather than published performance figures.The review’s median coverage was 61% and flagged 13 cases for re-collection; the architecture includes immutable originals, logged transformations and human gates.
  • Limitations: The analysable Corpus includes 12.1 million machine-translated English words without systematic human validation, so analyses inherit translation-engine biases.Immutable originals permit regeneration and re-checking, but the paper describes this as mitigation rather than a solution.
  • Limitations: Worldwide standardisation enables comparability across 686 cases but replicates the instrument’s blind spots, while the Corpus is not a probability sample.The geography reflects available partners, interviewers and opportunities; Colombia accounts for nearly a quarter of cases and ten countries for over four-fifths.
  • Limitations: The paper’s durable claims concern provenance, gates and immutability rather than the rapidly replaceable tools in this pipeline.The described chain is explicitly presented as a snapshot of a fast-moving system.

9 Conclusion

The Socioscope shows that LSQR requires an integrated, provenance-preserving pipeline and substantial organisational effort, but can make large-scale qualitative analysis feasible. Its first-phase corpus demonstrates the resulting scale and supports analyses unavailable to preformatted instruments.

  • Pipeline and provenance: A complete pipeline moves every case through scouting, enrolment, collection, formatting, quality control and curation with statuses, logs and traceable provenance.Originals remain immutable, and transformations are logged for each case, the Corpus and project documentation.
  • AI-enabled analysis: Concept extraction at scale is possible with high-end off-the-shelf LLMs and programming, making coding-grid tests and alternative analytic lenses much faster.The paper reports that tasks taking months or years by hand can be tested in a couple of hours.
  • Organisational requirements: Scaling qualitative research is a different organisational and epistemic undertaking requiring industrialised collection, structured data management, quality control and continuous technical updating.The processing chain reached its tenth prototype after ten months, reflecting changing techniques around stable research data.
  • Corpus and analysis: 686 cases from 31 countries produced about 1,430 hours of recordings and 12.6 million transcript words in under three years.The corpus also contains video material of similar magnitude whose analytical use beyond illustration remains to be explored.
  • Analytical possibilities: The corpus supports building typologies and ontologies, testing hypotheses formed on few cases across hundreds, and relating micro, meso and macro levels.The analysis can also reveal variables and answer questions that were not asked when the corpus was collected.
  • Lessons: The authors conclude that scale is organisational before technical, provenance makes a corpus scientific and auditable, and LSQR is costly but feasible and worth doing.They identify access, coordination, consent, quality control and payment as binding constraints rather than storage or compute.

Glossary

The glossary defines Socioscope concepts for sampling, data collection, infrastructure, provenance and social organisation. Together, these terms specify how initiatives become documented, traceable cases in an increment-only Corpus.

  • Corpus structure: The Corpus is an increment-only repository of digitised, documented and archived cases in which originals are immutable and nothing is erased.A SID uniquely identifies each speech turn and traces findings back to verbatim material.
  • Pipeline and provenance: The data pipeline carries a source from scouting to correctly formatted, labelled and metadata-rich storage in the Corpus, with provenance documenting collection and every transformation.A snapshot is a frozen, immutable, citable Corpus state; the Gate stores the Corpus and logs access and operations.
  • Social settings: An installation is a local setting that supports and controls predictable behaviour through affordances, embodied competences and institutions.An affordance is an object's potential for action for a subject, while embodied competence links perception to relevant action.
  • Core concepts: LSQR collects open-ended qualitative data on many cases with one protocol and uses AI to make the material analysable at scale.An initiative is the sampling unit, while a case is the material collected about that initiative.
  • Social organisation: A social contract combines a role and status, operationalised as balanced exchange in which participants provide deep access and receive media, membership and recognition.Roles and statuses describe behaviours people can legitimately expect from others and others can expect from them.
  • Sampling and data collection: The transaction grid records each stakeholder an initiative transacts with and what each party gives and gets, while zones of interest densify territorial sampling.Panelisation enables repeated data collection and new questions as research questions evolve.

Author contributions (CRediT)

The CRediT contributions assign the paper's work across conceptualisation, fieldwork, methodology, administration, curation, software, validation, resources and writing. They also document the editorial use of Claude separately from the AI processing pipeline studied in the paper.

  • Contributors: Saadi Lahlou led conceptualisation, formal analysis, funding, fieldwork, methodology, administration, supervision and manuscript writing as principal investigator.
  • Contributors: Juan Pablo Caicedo contributed data curation, fieldwork, project administration, communication, legal arrangements, resources and manuscript editing.
  • Contributors: Shriya Sekhsaria contributed version-ledger curation, the AI processing chain, Gate and website software, validation studies and manuscript editing.The listed validation work includes the 2024 human-versus-LLM benchmark and inter-rater study.
  • Contributors: Valentine Fournand contributed case-log and dashboard curation, fieldwork, feedback synthesis, administration, resources and manuscript editing.
  • Contributors: Paulius Yamin contributed conceptualisation, fieldwork, field protocols, topic-guide methodology, budget management, interviewer training and manuscript editing.
  • Contributors: Helga Nowotny contributed conceptualisation, formal analysis, funding and partnerships, fieldwork, research design, supervision and manuscript editing.

Appendices

The appendices provide access to the detailed Data Collection Protocol and its accompanying training materials. The protocol's contents are reproduced in the paper, followed by Appendices 1–7.

  • Protocol: The Data Collection Protocol is version 8 and spans 55 pages.
  • Materials: The protocol and four accompanying training videos are available from the authors on reasonable request.
  • Appendix structure: The protocol's table of contents is reproduced as Appendix 7, and Appendices 1–7 follow.

Appendix 1. Before attempting LSQR: sixteen rules in five families

The paper identifies sixteen rules across five families and recommends settling them before starting a comparable LSQR project.

  • Sixteen rules across five families should ideally be settled before attempting a comparable project.The appendix table records when each rule must be decided, the cost of getting it wrong, and the supporting evidence.

●1. Size and shape are design parameters, not dials

Scale is an initial design choice that determines the organisation required to deliver the project and propagates through its downstream arrangements.

  • Scale is chosen at the outset, and determines the budget, team, technology, and contracts required.The paper treats these downstream elements as consequences of the initial sizing decision.

●1.1. Determine the size of the endeavour before costing it

LSQR sizing requires considering both per-case and total costs because expanding the number of cases changes the economics and management regime.

  • More cases lower unit cost but increase total project cost, so both figures must be considered before starting.The paper warns that implicit sizing adopts a management model by accident.

●1.2. Derive the technology/human split from size and research objective

The technology–human split should follow project size and research objectives, with automation concentrated in mechanical work and human responsibility retained for judgement, rapport, and accountability.

  • 1.2. Derive the technology/human split from size and research objective: A proof of concept should test interviewers, equipment, transcription, upload, and quality control on tens of cases before scaling.It provides measured unit cost and cycle time, and clarifies what the analysis requires before fieldwork expands.
  • 2.1. Settle contractual infrastructure before collection: Contracts must be written before data collection because they cannot be renegotiated once the data exists.At scale, contracts are treated as infrastructure.
  • 2.1. Settle contractual infrastructure before collection: A fifteen-year exploitation period was obtained instead of the default three-to-five-year period, extending the corpus’s useful life.The decision is made before collection and also affects researchers who join later.
  • 2.2. Build the legal and ethical infrastructure ahead of the field: Ethics, privacy, data-controller roles, confidentiality, and breach procedures must be approved for every jurisdiction before collection opens.The required infrastructure grows with the intended corpus lifetime and geographic scope.
  • 2.3. Design participant and interviewer obligations before collection: Participant follow-up obligations and payment linked to quality should be established before fieldwork, because retrofitting them is costly or impossible.The invoice-triggered quality-control clause makes payment follow confirmed quality rather than upload completion.
  • 3. Keep the core central, and partner for the rest: Scaling requires keeping protocol, quality control, initiative relationships, and corpus ownership central while delegating other work through settled partnerships.The appropriate partnership form depends on project size and objective.
  • 3.1. Select and train for compliance: Interviewer selection should be empirical, with training focused on teamwork and protocol compliance and compliance monitored continuously.In the proof of concept, very few of thirty tested interviewers were retained.
  • 4.1. Put the automation line where the configuration requires: Automation should target transcription, naming, and tracking, while humans retain outreach, interviews, go/no-go decisions, and final sign-off.The proposed division is the operational expression of a split derived from project size and research objectives.
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