{"compiledCode":"\"use strict\";Object.defineProperty(exports, \"__esModule\", {value: true}); function _interopRequireDefault(obj) { return obj && obj.__esModule ? obj : { default: obj }; }var _react = require('react'); var _react2 = _interopRequireDefault(_react);\n\n function ResearchPage() {\n  const d = {\"h1\":\"Research study and dataset specification\",\"intro\":\"An evidence specification for category visibility indexes and model-behaviour studies, covering prompt sets, model versions, run dates, sample size, variance, and reusable data.\",\"primaryCtas\":[{\"label\":\"Use the reproducible study specification\",\"href\":\"#what-every-study-publishes\"},{\"label\":\"Read the measurement method\",\"href\":\"/methodology\"}],\"notice\":\"RankRift has not published a study or dataset. This page is the evidence bar a study must meet, whether you are designing your own or checking someone else's.\",\"inShort\":\"This is a vendor-neutral specification for recurring, reproducible studies of how AI assistants name and cite brands. It covers category visibility indexes and model-behaviour studies. A qualifying study publishes its prompt set, model identifiers and versions, run dates and counts, variance, results, method changes, reusable data, citation string, re-run cadence, and dispute route.\",\"whoFor\":[\"In-house SEO and growth leads who need category-level evidence rather than a single anecdote about their own brand\",\"Agency and consultant strategists building a client point of view on a category they do not yet have data for\",\"Content and PR leads deciding which publications and source types are worth pitching\",\"Journalists and analysts who need to know what a citable figure must carry\",\"Anyone who has been shown a vendor chart and wants to know what a checkable version looks like\"],\"whenToUse\":[\"When you need a category-wide picture instead of a reading on one brand\",\"When you are evaluating whether any published AI-visibility figure meets a reasonable evidence bar\",\"When you want a template for a study you intend to run yourself\",\"When a study has named your brand and you want to check the figure or dispute it\"],\"sections\":[{\"heading\":\"What this page provides\",\"paragraphs\":[\"This page publishes the study specification, not a results archive.\",\"The specification stands on its own: use it to design a run, assess a published study, or reject a result that omits the evidence needed for reproduction.\",\"For measurement instructions, use the methodology and the measurement guide.\"],\"bullets\":[{\"text\":\"Read the measurement methodology\",\"href\":\"/methodology\"},{\"text\":\"Run the manual measurement procedure\",\"href\":\"/measure\"}],\"note\":\"A complete protocol is useful on its own; an unsupported results claim is not.\"},{\"heading\":\"What every study publishes\",\"paragraphs\":[\"The evidence bar is fixed in advance so that no individual study can quietly drop the inconvenient parts. If any of the following is missing, the study does not publish.\",\"This list is also usable as a checklist against anyone else's research, including a vendor's. Most published AI-visibility figures in this category fail it on the first two lines.\"],\"bullets\":[{\"text\":\"The complete prompt set, with its version identifier, not a description of it\"},{\"text\":\"Every model identifier and version queried, plus the interface used\"},{\"text\":\"Run dates and the number of runs behind each figure\"},{\"text\":\"Variance for every figure, and the interval method used to express it\"},{\"text\":\"The metric definitions and method version in force at run time\",\"href\":\"/methodology\"},{\"text\":\"A changelog against the previous run, naming what moved and what changed in the method\"},{\"text\":\"Downloadable CSV and JSON under an open licence, with a required citation string on the page\"},{\"text\":\"A route to dispute the figures and request a re-run\",\"href\":\"/corrections\"}]},{\"heading\":\"The two study types\",\"paragraphs\":[\"A category visibility index answers a market question: across a defined set of buyer prompts in one category, which brands do assistants name, how often, alongside whom, and from which cited sources. It is comparative, so the competitor set and the prompt set are stated up front and the study is only meaningful in their terms.\",\"A model-behaviour study answers a mechanism question: how does a given assistant assemble and attribute an answer, how stable is it across phrasings and repeats, how does it treat freshness, and how do two assistants differ on identical inputs. These are narrower, usually cheaper, and often the more useful of the two for anyone designing their own measurement.\",\"Both types are designed to be re-run rather than to be a one-off headline. A study that is never repeated tells you about one week.\"],\"bullets\":[{\"text\":\"How assistants differ in the way they source an answer\",\"href\":\"/blog/how-assistants-source-answers-compared\"},{\"text\":\"Why the same question returns different brands to different people\",\"href\":\"/blog/why-ai-answers-vary-between-people\"},{\"text\":\"Which sources decide the recommendations in your category\",\"href\":\"/blog/who-decides-your-category\"}]},{\"heading\":\"Category-admission worksheet\",\"paragraphs\":[\"Before starting a category study, write down its re-run budget and cadence. Reject a category that cannot be re-run on schedule, because a stale index can keep being cited after it stops describing current outputs.\",\"Admit a category only when the buyer question is genuinely asked of assistants, the competitor set has a documented boundary, the prompt language is stable enough to preserve a time series, and the reporting language cannot reasonably be mistaken for a quality verdict on a small business.\",\"Record who proposed the category, who funded the run, whether any subject has a commercial relationship with the publisher, and the reason for inclusion. Paid inclusion, ordering, or timing fails the admission test. A funder cannot be a subject of the study it funds.\"],\"bullets\":[{\"text\":\"Read the prompt-set design rules\",\"href\":\"/competitive/prompt-sets\"},{\"text\":\"Apply the commercial-conflict test\",\"href\":\"/disclosure\"}]},{\"heading\":\"Re-run requirements\",\"paragraphs\":[\"Require every published study to state its re-run cadence and next scheduled run date. Re-runs use the frozen prompt-set version wherever possible, so a change in the figures reflects a change in the answers rather than a change in the questions.\",\"When a prompt set genuinely has to change, the new version is published, the break in the trend is shown explicitly, and the old series is not silently stitched to the new one. When the method version changes, the changelog says which published figures are affected.\",\"If a study misses its schedule, mark it stale on the study page rather than leaving it standing as though it were current.\"],\"bullets\":[{\"text\":\"Setting a baseline you can still defend six months later\",\"href\":\"/blog/defensible-baseline\"},{\"text\":\"Tracking a competitor's movement without inventing an explanation\",\"href\":\"/blog/tracking-competitor-movement\"}]},{\"heading\":\"Measured visibility is not a quality verdict\",\"paragraphs\":[\"This deserves its own section because it is the most common misreading and the one with real consequences for the brands a study names.\",\"When a study reports that an assistant named one brand more often than another, that is a factual statement about model output under stated conditions. It is not a statement that one product is better, more reputable, or more suitable for you. Assistants reflect the text that exists about a category, and that text is shaped by coverage, category vocabulary, entity clarity, the age of source material, and how legible a brand is to a retriever. A strong product with poor entity clarity can be nearly invisible.\",\"Phrase qualifying studies as observations about models, never as rankings of merit, and reject 'best of' framing derived from visibility measurement. Treat language that implies a product-quality verdict as a reporting error.\"],\"bullets\":[{\"text\":\"When a mention is not a recommendation\",\"href\":\"/blog/mention-vs-recommendation\"},{\"text\":\"Benchmarking against competitors without fooling yourself\",\"href\":\"/blog/fair-benchmarking\"}],\"note\":\"General information only, not legal advice: if a published figure raises a legal question for your brand, consult qualified counsel.\"},{\"heading\":\"Dispute and re-run procedure\",\"paragraphs\":[\"Every qualifying study provides a route for a named brand to identify the study, disputed figure, and alleged measurement error. The publisher then re-runs the relevant slice under the published method rather than negotiating the value.\",\"Log the dated outcome either way and publish a re-run that supports the original figure as well as one that changes it. Dislike and payment are not grounds to remove or change a correctly measured figure, placement, or ordering.\",\"Classify disagreements with the method separately from run errors. A valid method change produces a new method version and identifies the figures affected by the change.\"],\"bullets\":[{\"text\":\"The correction and dispute protocol\",\"href\":\"/corrections\"},{\"text\":\"Where each kind of question is answered\",\"href\":\"/contact\"}]}],\"related\":[{\"label\":\"The measurement methodology\",\"href\":\"/methodology\"},{\"label\":\"How to measure AI visibility yourself\",\"href\":\"/measure\"},{\"label\":\"Prompt-set patterns by category\",\"href\":\"/competitive/prompt-sets\"},{\"label\":\"Competitive and category intelligence\",\"href\":\"/competitive\"},{\"label\":\"Corrections and disputes\",\"href\":\"/corrections\"},{\"label\":\"Briefing template\",\"href\":\"/newsletter\"}],\"faqs\":[{\"q\":\"Why does this page publish a specification instead of rankings?\",\"a\":\"Because a protocol is useful without pretending to be a dataset. Plenty of figures circulate without a prompt set, model version, or sample size; this page gives readers a concrete evidence bar for assessing them.\"},{\"q\":\"What licence should study data carry, and how should I cite it?\",\"a\":\"A qualifying study supplies CSV and JSON under a clearly identified licence and displays its exact citation string. A reader must be able to recompute the headline figures and publish a different interpretation within that licence. Record the dataset version and retrieval date in the citation.\"},{\"q\":\"Can I sponsor a study or pay to be included in a category index?\",\"a\":\"Paid inclusion, placement, and ordering fail this specification. A funder must be identified, cannot appear as a subject of the funded study, and cannot influence prompt sets, categories, results, or sequencing.\"},{\"q\":\"How is a category index different from running the manual snapshot on my brand?\",\"a\":\"The snapshot samples a handful of prompts for one brand and produces a point-in-time reading with a wide confidence range. An index measures a whole competitor set across a versioned prompt set at a defined sample size, on a schedule, with variance reported and the data published. One is a quick read on your own situation; the other is evidence about a market that other people can reuse.\"},{\"q\":\"How should a study handle an unfavourable result about a named brand?\",\"a\":\"Report what the models produced without presenting visibility as a judgement of quality. A study naming brands must state the prompt set and run conditions and carry its dispute and re-run route on the page. When a reproducible error changes a result, the dated re-run stands as the correction.\"},{\"q\":\"How often should studies be re-run?\",\"a\":\"Set cadence before the first run and publish the next scheduled date. A category with fast-moving coverage needs more frequent re-runs than a stable one. Do not launch a time series without the resources to sustain that cadence; visibly mark a missed re-run stale.\"},{\"q\":\"Can I use the prompt patterns for my own measurement?\",\"a\":\"Yes. Build a set from the patterns, freeze the version you used, run it against your own model roster, and record the versions and dates alongside your results. If you adapt it later, treat the adapted set as a new version so your own trend line stays honest. The patterns and the design rules for adapting them are on the prompt-set page.\"}],\"closingCtaHeading\":\"Use the evidence bar before citing a study\",\"closingCtaText\":\"Apply the evidence bar to a proposed or published study before relying on its result.\",\"closingCtas\":[{\"label\":\"Read the methodology\",\"href\":\"/methodology\"},{\"label\":\"Run the manual procedure\",\"href\":\"/measure\"}]};\n  const btn = (kind) => ({\n    display: \"inline-block\",\n    padding: kind === \"primary\" ? \"11px 18px\" : \"9px 16px\",\n    margin: \"6px 10px 6px 0\",\n    borderRadius: 8,\n    textDecoration: \"none\",\n    fontWeight: 700,\n    fontSize: 15,\n    background: kind === \"primary\" ? \"#3d3f8c\" : \"transparent\",\n    color: kind === \"primary\" ? \"#ffffff\" : \"#3d3f8c\",\n    border: \"2px solid #3d3f8c\",\n  });\n  return (\n    _react2.default.createElement('div', { className: [\"np-rankrift\",\"rrpage\"].join(\" \"), 'aria-labelledby': \"rr-title\",}\n, _react2.default.createElement('style', null, \"\\n        .np-rankrift.rrpage{max-width:880px;margin:0 auto;padding:28px 20px 64px;color:#1a1f2b;\\n          font-family:var(--cdqcat5-font-body, \\\"DM Sans\\\", Arial, sans-serif);\\n          line-height:1.62;font-size:17px}\\n        .np-rankrift.rrpage h1{font-size:2rem;line-height:1.2;margin:0 0 12px}\\n        .np-rankrift.rrpage h2{font-size:1.35rem;line-height:1.3;margin:36px 0 10px}\\n        .np-rankrift.rrpage p{margin:0 0 14px}\\n        .np-rankrift.rrpage ul{margin:0 0 16px;padding-left:22px}\\n        .np-rankrift.rrpage li{margin:0 0 8px}\\n        .np-rankrift.rrpage a{color:#33368a}\\n        .np-rankrift .rr-intro{font-size:1.16rem;color:#39414f}\\n        .np-rankrift .rr-callout{background:#f2f3fb;border-left:4px solid #3d3f8c;padding:12px 16px;border-radius:6px;margin:6px 0 16px;font-size:0.98rem}\\n        .np-rankrift.rrpage details{border:1px solid #e3e5ef;border-radius:8px;padding:10px 14px;margin:0 0 10px;background:#fbfbfe}\\n        .np-rankrift.rrpage summary{cursor:pointer;font-weight:600}\\n        .np-rankrift.rrpage details p{margin:10px 0 2px}\\n        .np-rankrift .rr-ctarow{margin:16px 0 10px}\\n        @media (max-width:560px){.np-rankrift.rrpage{font-size:16px;padding:22px 16px 56px}.np-rankrift.rrpage h1{font-size:1.6rem}}\\n      \")\n\n      , _react2.default.createElement('h1', { id: \"rr-title\",}, d.h1)\n      , _react2.default.createElement('p', { className: \"rr-intro\",}, d.intro)\n      , _react2.default.createElement('div', { className: \"rr-ctarow\",}\n        , d.primaryCtas.map((c, i) => (\n          _react2.default.createElement('a', { key: i, href: c.href, style: btn(i === 0 ? \"primary\" : \"ghost\"),}, c.label)\n        ))\n      )\n\n      , d.notice ? _react2.default.createElement('div', { className: \"rr-callout\",}, d.notice) : null\n\n      , d.inShort ? 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