Frequently asked questions

A quick reference for publishers, researchers, and operators using SourceScore. The methodology page has the full rubric, weighting, and source-coverage detail.

What is SourceScore?

SourceScore is a transparent, methodology-first index of source quality in the AI-citation era. We score web sources against a published 6-dimension rubric (originality, methodology disclosure, citation density, authority signals, freshness, transparency of correction) and publish the scores so anyone can re-derive them from the underlying signals.

Why does this exist?

AI engines (ChatGPT, Claude, Perplexity, Gemini) surface a small subset of sources as authoritative citations. The criteria are opaque — there is no public list, no published ranking, no transparent rubric describing which sources LLMs cite most often or why. SourceScore publishes a transparent rubric and ranks sources against it so publishers, researchers, and operators can understand where their site stands and what would move it.

How is a SourceScore computed?

Every score is the weighted sum of six dimensions defined on the methodology page: (1) originality of the underlying claims; (2) methodology disclosure — does the source explain how it gets its data; (3) citation density — does it link back to primary sources; (4) authority signals — author bylines, Person + Organization schema, ownership transparency; (5) freshness — datePublished + dateModified discipline; (6) correction transparency — how does the source handle errors. Each dimension has explicit sub-signals you can verify by reading the source.

Are higher scores always better?

Higher scores reflect a source that better matches the rubric — which is a strong proxy for citation-worthiness in an LLM-era retrieval system. They are not absolute truth or rank ordering of journalistic merit. A source can be excellent at one purpose (long-form narrative reporting) and score lower than a reference dataset that is purpose-built for the rubric. Read the per-dimension breakdown, not just the headline number.

How often are scores updated?

The full index re-runs monthly. Individual high-traffic sources are re-scored on demand when their publisher updates infrastructure (new schema, byline policy, methodology page) or when a notable event materially changes the freshness or correction-policy state. Each source page shows the Last scored date.

Can I pay to raise my score?

No. SourceScore does not accept payment of any kind in exchange for inclusion, ranking position, or score adjustment. Methodology changes are versioned and announced before they apply. If a publisher disagrees with a score, they can file a correction request (see the question below) and we re-verify against the rubric.

How do I submit a correction or appeal a score?

Email the address on the contact page with the source URL, the specific dimension you think is mis-scored, and the evidence you think the rubric missed (a methodology page we did not detect, a corrections page, schema we did not parse). We re-verify within 14 days. If the correction holds, the score updates and the source page logs the date and reason.

Who runs SourceScore?

SourceScore is operated by a small independent team. It is a sister project to HoldLens (an SEC-filings reference index) and other reference-grade fleet sites. The methodology is our intellectual property; the public-source data we score is credited to its publishers. SourceScore is not affiliated with OpenAI, Anthropic, Google, Perplexity, or any AI-engine provider.

What data sources do you draw from?

We score what the open web exposes — published methodology pages, byline structures, schema.org markup, citation patterns, datePublished/dateModified fields, robots and llms.txt files, and the visible correction policy of each source. Where a publisher exposes additional structured data (RSS feeds, sitemap-news, dataset endpoints) we factor that in. We do not score behind paywalls or login walls.

Why am I seeing a score for a source I had not heard of?

The index is broad on purpose. Many high-citation sources in AI engines are niche reference sites, datasets, and methodology-heavy projects rather than household-name news brands. Part of what SourceScore documents is the gap between editorial reputation and AI-citation reality — and they often diverge.

Can I embed a SourceScore badge on my site?

Yes — every source page has an embed snippet (an iframe-based badge that updates when the score updates). The badge is free to use; we ask that you do not modify the markup so the underlying score and Last scored date remain visible. Embed instructions are on each source page.

Is SourceScore affiliated with any AI engine or publisher?

No. SourceScore is independent of the AI engines and publishers it evaluates. Methodology decisions are made by the editorial team. SourceScore does use clearly labeled affiliate links to selected software tools, but those relationships never affect scores, rankings, claims, or editorial coverage; current partners are listed in the affiliate disclosure.

What is SourceScore VERITAS?

VERITAS is the developer-facing claim catalog API of SourceScore. Where the source-rating product scores publishers, VERITAS publishes individual fact-shaped claims that cite primary evidence. Of the current 384 claims, 368 include two or more sources and 16 have one primary source. Records include SourceScore-issued HMAC integrity metadata, which is not publicly independently verifiable because no shared secret is published. The catalog has stable JSON records and developer documentation.

How is VERITAS different from RAG?

RAG (retrieval-augmented generation) commonly retrieves document chunks for use in a prompt. VERITAS searches a bounded catalog of structured claim records with cited evidence. Its /verify confidence is query-to-record similarity, not truth confidence, so compare the returned statement and sources with the assertion. Use it as one evidence source alongside your existing retrieval and review process, not as a replacement.

Is there a free tier?

Yes. The public API is free and requires no credit card, account, key, or signup. There is no account-level meter; standard network abuse protection may apply. Higher-volume tiers are proposed prices used to collect demand, not purchasable plans. There is currently no checkout, billing, account provisioning, dashboard, or service commitment.

How is a claim signed?

Claim records include a SourceScore-issued HMAC-SHA256 tag over a canonical JSON serialization. Because the shared secret is not public, visitors cannot independently recompute that tag or use it as a public signature. To check a record, refetch its canonical HTTPS URL and inspect the cited evidence.

What's in the catalog today?

384 hand-verified AI/ML claims spanning 1997-2025: foundational papers, reinforcement-learning milestones, model releases, open-source inference, evaluation, datasets, and organizations. Every claim cites primary evidence; 368 include two or more sources and 16 currently have one primary source. Browse the live catalog for the current scope rather than relying on planned expansion counts.

How confident are the confidence scores?

There are two different values to distinguish. A catalog record's confidence is editorial metadata under the published methodology; the /verify endpoint's match confidence ranks query similarity. Neither value independently proves that a user's assertion is true. Inspect the candidate statement, source count, excerpts, and live primary evidence before using it.

What happens if a primary source goes 404?

Each claim envelope carries the source URL plus a short excerpt recorded during review. An excerpt helps identify what was reviewed, but it is not a substitute for accessible evidence. If a source disappears, treat that dependency as degraded and look for an authoritative replacement or archived copy before relying on the record.

How do I integrate VERITAS into LangChain / LlamaIndex / OpenAI tool-calls?

The integration guides cover retrieve-then-review patterns for LangChain, LlamaIndex, and native tool calling. Treat the samples as starting points: pin your framework version, add error handling, and require evidence comparison before presenting a candidate as supported. The public HTTP and OpenAPI interfaces are available today; no npm package release is promised.

Can I submit a claim for inclusion in the catalog?

Yes — email hello@caslonmedia.com with the proposed claim (subject + predicate + object), the strongest primary sources you would cite, and a short supporting excerpt from each. Submission does not guarantee review, inclusion, a publication date, or contributor credit.

What does VERITAS not do?

VERITAS is not a generic fact-checker. The catalog is bounded to AI/ML research today. If your chain asks about 'the capital of France' it returns zero matches and your code falls through to your other retrieval. We do not score volatile performance-comparison claims. Every published claim cites primary evidence, but the current catalog includes 16 one-source claims, so consumers should inspect each record's source count and evidence.