Journal Atlas: Venue Fit Scoring Under Incomplete Evidence, and the Corpus Method It Requires

Abstract

We present Journal Atlas, a public corpus of 399 curated venue profiles and two methods over it: one that scores how well a manuscript fits a venue and reports how much evidence that score rests on, and one that builds the corpus with a language model without letting it invent what nobody has recorded. Existing recommenders match title and abstract against published articles and return topically similar venues, but what decides whether a submission survives is largely absent from any index: whether a stated word limit is enforced, whether a reviewer pool treats a method as a defect. The most systematic comparison of twenty such services closes by observing that background knowledge is still required for the final decision. Asking a language model instead substitutes fluency for evidence, where fluent-but-wrong is measured rather than suspected. Hard constraints eliminate, six weighted dimensions rank what survives, and a dimension without evidence returns unknown rather than a midpoint. A score above neutral is then shrunk in proportion to the evidence missing beneath it, and missingness is never allowed to raise one. A recommendation therefore arrives as a score and an evidence-coverage figure. Corpus construction follows one rule: a fact with a source URL, or a blank with a recorded reason. Coverage measured across the corpus splits on the tier boundary, 163 curated entries at a median 100% against 236 at 40%, the latter missing the same four dimensions — the ones an automated pass could not recover reliably from public sources. A live interface demonstrates the pipeline without account or installation. We name what the corpus does not contain, and make no claim about acceptance outcomes, which we have not measured.