Yes — unlimited, no signup. It's bundled into the free Pre-Check tool. Because it runs on a local index with no LLM call, each search costs us effectively nothing, so there's no rate-limit games and no upsell on the recommender itself.

Does it warn me about predatory journals?

Yes. Every candidate is checked against a snapshot of Beall's List of predatory publishers plus a name-pattern heuristic. Flagged journals still appear — you keep the choice — but with an inline warning and a link to the evidence. There's also an 'exclude predatory' filter to drop them entirely.

How is this different from JANE or a publisher's journal finder?

Two big differences. First, it's publisher-neutral — Elsevier's, Springer's, and Wiley's finders only show you their own journals; ours ranks across 1,214 venues regardless of publisher. Second, it shows its work: every recommendation comes with the specific similar papers that drove the match, the journal's tier, and a predatory flag — not just a name and a percentage.

Why don't you show the real Impact Factor?

The official Journal Impact Factor is a proprietary Clarivate metric behind a paywall — we can't redistribute it. Instead we show the 2-year mean citedness from OpenAlex, which is open, comparable across venues, and measures the same thing the IF is a proxy for: how often recent articles get cited.

Does Science AI Journal recommend itself?

It can — but only through the same BM25 retrieval every other journal goes through, with no special boost. If your paper's topic genuinely matches the cohort we've published, our own journal will surface in the ranked list on its merits, the same way any well-matched venue does. If it doesn't match, it won't appear. The ranking is transparent and the 'why this journal' evidence applies to us too.

Can I filter to open-access journals only?

Yes. There's a one-click 'open access only' filter, plus filters for minimum tier, excluding predatory venues, and excluding preprint servers. You can also sort by best match, highest citation rate, fastest decision, or most accepting.

Where do I actually run it?

It's built into the free Pre-Check tool — paste your title and abstract there and the journal shortlist appears alongside your tier verdict. The same panel also runs inside the full AI Review, decorated with the additional signal from your complete manuscript.

Journal Recommender

Free Journal Finder — match your paper to the right venue

Picking the wrong journal costs months. Paste your title and abstract and the Journal Recommender ranks a shortlist of target venues from a 1,214-journal index — each one shown with its tier, open-access status, citation rate, publisher, and a predatory-journal flag. It runs on a local 33,976-paper library with zero LLM cost: free, unlimited, sub-second, no signup.

Find my journal — run a free Pre-Check Or check for duplicate publication first

1,214

Venues indexed

Cost per search

33,976 papers

Library behind it

Beall's-based

Predatory flag

What it does

The Journal Recommender answers the question every author asks after the draft is done: where do I send this? Instead of guessing, or paying a publisher's own finder that only shows you its own journals, you get a publisher-neutral ranked shortlist drawn from a 1,214-venue index — and every recommendation comes with the evidence behind it.

For each journal it surfaces six fixed metadata chips — open-access status, 2-year citation rate, tier, field profile, publisher, and a predatory-journal flag — plus a 'why this journal' panel that names the specific similar papers in our library that drove the match. You see the reasoning, not just a ranked list.

Ranked shortlist with a letter-grade match score (A–F) per journal.
Six metadata chips in stable positions: OA · citation rate · tier · field · publisher · predatory flag.
'Why this journal' — the actual similar papers that drove each match.
Publisher-neutral: it is not tied to one publisher's catalogue.
Filters: exclude predatory, open-access only, minimum tier, exclude preprints.
Sort modes: best match, fastest decision, highest citation rate, most accepting.

How it works (5 steps)

There is no LLM in the loop — the whole pipeline is retrieval and ranking over a local index, which is why it is free, instant, and deterministic:

Step 1. Paste your title and abstract — Open the free Pre-Check tool and paste the working title and abstract of your manuscript. Author keywords are optional but sharpen the match.
Step 2. BM25 retrieval over a 33,976-paper library — Your title and abstract become a full-text query against our local FTS5 index of 33,976 papers. The strongest topical matches are retrieved in milliseconds — no LLM call, no API cost.
Step 3. Reciprocal Rank Fusion by journal — The retrieved papers are aggregated to their journals and scored with Reciprocal Rank Fusion (k=60), so a venue that publishes many near-matches ranks above one with a single lucky hit.
Step 4. Metadata decoration and ranking — Each candidate journal is decorated with its tier, open-access status, 2-year citation rate, publisher, field profile, and a predatory-journal flag — then re-ranked with field, recency, and predatory signals.
Step 5. Read the shortlist with evidence — You get a ranked shortlist with a letter-grade match score, the six metadata chips, and a 'why this journal' panel showing the specific similar papers that drove the match. Filter and sort to taste.

Predatory-journal protection

A journal recommender that surfaces predatory venues without warning is worse than no recommender. Every candidate is cross-referenced against a snapshot of Beall's List of predatory publishers, plus a name-pattern heuristic for the tell-tale 'International Journal of …' clusters with thin publication histories.

When a journal is flagged, it still appears in the results — you keep the agency to make your own call — but with an inline warning above the title and a link to the evidence. The point is informed choice, not a hidden filter. You can also switch on the 'exclude predatory' filter to drop them entirely.

When to use it

The recommender is highest-leverage at these moments:

After the draft is done, before you commit to a target — compare three or four venues side by side.
After a desk rejection — find the next-best-fit venues without restarting the search from scratch.
When you're new to a field and don't yet know its journal landscape.
When a colleague suggests a journal you don't recognise — check the tier and the predatory flag before you trust it.
When you need an open-access venue specifically — the OA-only filter narrows the 1,214 down instantly.

What it does NOT do

Honest limitations, because a recommender you can't trust the boundaries of is a recommender you can't trust:

It does not report the official Journal Impact Factor — that metric is paywalled by Clarivate. We use the 2-year mean citedness from OpenAlex as an open, comparable proxy.
It does not predict acceptance rate or time-to-decision yet — no reliable open editorial dataset exists for that, so we don't fake it.
It only knows the 1,214 venues with enough coverage in our 33,976-paper library. A highly specialised journal with thin coverage may not surface.
It is a shortlist, not a decision. Scope, aims, and recent issues of the actual journal are still yours to read before you submit.

Frequently asked questions

It runs your title and abstract as a full-text BM25 query against a local index of 33,976 papers, retrieves the strongest topical matches, then aggregates those papers to their journals using Reciprocal Rank Fusion (k=60). A venue that publishes many near-matches ranks above one with a single lucky hit. There is no LLM in the loop — the result is deterministic and free.

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