How to Publish Original Research and Named Frameworks

Why it matters

Original research is the strongest citation magnet there is: a benchmark, dataset, or analysis from your own data is, by definition, available nowhere else, so an AI system that wants to answer the question has to come to you. But research alone is unclaimed IP — without a name and a structure, your finding gets absorbed and re-attributed. Conversely, a framework with no research behind it is just an assertion.

The two are inseparable. Publish the data and the named structure that organises it, and you create a knowledge asset that is both citable and ownable — the "AI Knowledge Signal Framework" rather than "some ideas about AI content".

What good looks like

Original findings, presented as a named, structured contribution with its methodology on show:

• You publish benchmarks, analyses, or observations from your own data.
• The methodology, sample size, and time window are stated — "In our analysis of [N] AI responses (Q3 2026)…".
• Every model, methodology, or taxonomy has a clear name.
• It's presented as structure — phases, a diagram, or an equation — not loose prose.
• You refer to it consistently by name across all content.

Step by step

1. Publish from your own data Run a benchmark, analysis, or observation on data you control and publish the result. Even a modest original dataset is more citable than a polished restatement of someone else’s work.
2. State methodology, sample size, and time window Show your working: what you measured, how many, and when. "In our analysis of [N] AI responses (Q3 2026)…" is what makes the finding verifiable and trustworthy (and feeds Phase 4.4 source quality). If you report token counts, name the tokenizer — the same text gives different counts under different vocabularies.
3. Name the framework or model Give the structure a clear, consistent name — "AI Knowledge Signal Framework", "[Your Field] Benchmark Report Q3 2026", or a named equation like AI Visibility = Extractability × Trust × Accessibility. A named thing can be cited and claimed; an unnamed one can't.
4. Present it as structure Express the framework as defined phases, a diagram, a table, or an equation — something with shape. Structured IP is easier for both humans and models to grasp, reuse, and attribute.
5. Refer to it consistently by name Use the exact same name everywhere it appears — site, blog, newsletter, schema. Consistent naming is what builds the entity over time (Phase 5.2 / 5.3) so the framework becomes associated with you.

Make the research reusable

Research is cited more when others can check and reuse it:

• Publish the underlying data as a download (CSV), not only charts.
• Describe it with Dataset markup (name, description, creator, licence, date range).
• Give significant reports a persistent identifier — for example a DOI via Zenodo.
• State a licence (e.g. CC BY 4.0) so reuse with attribution is explicitly allowed.

Worked examples

Finding:     41 of 120 tracked prompts (34%) cited
             at least one of our pages.
Method:      120 prompts, each run 5 times on ChatGPT,
             Claude, Gemini and Perplexity (web search on).
Window:      1–30 September 2026.
Data:        CSV of every prompt, run and cited URL.
Framework:   Reported under the AI Knowledge Signal
             Framework, Phase 6.3 (citation monitoring).

Common mistakes