SEA Low-Resource AI Measurement · Research Collaboration
We hold data academia cannot get; academia holds ground truth we cannot buy. An open invitation to researchers of Vietnamese, Thai, Indonesian, Malay and Traditional-Chinese to publish on real AI-answer corpora.
NeuronSpark (觀瀾智庫) operates a continuously running generative-engine measurement pipeline: every month, thousands of native-language buyer questions go to ChatGPT, Gemini, Perplexity, Claude and the real Google AI Overview surface. Full answer text, citation links and structured verdicts are preserved, and a human blind-labelled golden set calibrates the machine judge.
Longitudinal, multi-engine, human-anchored corpora in SEA languages have no public equivalent. We open them to serious academic collaboration — anonymized data and co-authorship, in exchange for native-speaker ground truth and methodological rigor.
Four research-ready assets
Months of full multi-engine AI answers with machine labels, plus a blind-annotation UI and inter-annotator κ gates — built precisely for human–machine agreement research.
Fact-consistency instrument testing C and ¬C statement pairs with round-stability detection; method page is public. First finding: 12.2% divergence between Chinese scripts — awaiting replication in more languages.
Every scan persists all cited links, aggregated into "who AI actually cites in this market"; the Vietnam source list is published monthly, with a T1/T2/T3 tiering standard in production.
Timestamped interventions (content publication, third-party citation building) matched against raw samples before and after — causal material for AI-visibility research.
Six ready-made research directions
Every pipeline below already runs in production — collaborators have real data from day one.
A fair exchange, not outsourcing
- · Anonymized research corpora and a live measurement instrument
- · Co-authorship with contribution roles agreed in writing upfront
- · Annotation stipends and conference travel support
- · Engineering support: labelling UI, data export, replication scripts
- · Native-speaker annotation and ground-truth calibration (vi/th/id/ms/fil)
- · Methodological rigor and reviewer-grade criticism
- · Joint grant applications (RGC/GRF etc., with us as industry partner)
- · Findings written into publishable papers
Four collaboration formats
Native-speaker students join blind labelling and QA; contribution thresholds earn a place on the author list. Ideal for a first paper.
Co-design a study on any direction above; we bring data, instruments and engineering — you bring methods and students.
Anonymized golden subsets curated into public datasets, published along SEACrowd-style community routes.
One event covers recruitment, labelling and method dissemination — our established event format.
FAQ
A one-page data-use agreement first: brand and client identifiers anonymized, data restricted to research use, anonymization re-checked before publication. The corpus is mostly public AI engines answering public questions — the privacy surface is inherently narrow.
Agreed in writing at kick-off: who contributes data, methods, annotation, writing — and the ordering that follows. No honorary authorship, in either direction.
We cover annotation stipends and necessary travel — subsidy scale, not grant scale. Formal funding is best applied for by faculty (RGC/GRF etc.), with NeuronSpark as the industry partner providing data and infrastructure.
Whether a machine judge is right is ultimately for native speakers to decide. And papers that study who AI cites become, themselves, high-authority sources AI cites. Academic collaboration solves ground truth and credibility at once — the honest way to run a measurement business.
Express interest
Leave your direction and contact — we reply within five working days.