> ## Documentation Index
> Fetch the complete documentation index at: https://docs.consensus.app/llms.txt
> Use this file to discover all available pages before exploring further.

# Find which journals to submit to

> Identify the journals actually publishing work like yours, with their quartile, so you target a realistic venue first.

## What you get

A shortlist of journals that publish your kind of work, each with how many closely-related papers they carry, their SCImago quartile, and representative papers you can read to judge fit.

## Who it's for

Researchers choosing a submission target — especially early-career authors, and anyone publishing into a field they are new to. A mis-targeted submission costs a months-long rejection cycle before you learn anything.

## How it works

<Steps>
  <Step title="Search as if you were looking for your own paper">
    Query the specific contribution, not the broad topic. "Machine learning in healthcare" returns the wrong venues; "transformer models for ICU mortality prediction" returns the ones that would review you.
  </Step>

  <Step title="Sweep deep, not wide">
    Paginate. The venue distribution in the top 20 results is noisier than across 200, and specialist journals surface further down.
  </Step>

  <Step title="Tally journals with quartiles">
    Count `journal_name` across the deduplicated set, carrying `sjr_best_quartile` and `publisher_name`. Frequency tells you where this conversation happens; quartile tells you how hard the door is.
  </Step>

  <Step title="Split by recency">
    Rank once over five years and once over the last eighteen months. A journal that published heavily in 2019 and nothing since has moved on from the topic.
  </Step>

  <Step title="Read three papers per candidate">
    Fit is about framing and method, not keywords. The representative papers are the point of the exercise.
  </Step>
</Steps>

## The API call

```bash theme={null}
curl -G "https://api.consensus.app/v1/search" \
  -H "x-api-key: $CONSENSUS_API_KEY" \
  --data-urlencode "query=transformer models ICU mortality prediction electronic health records" \
  --data-urlencode "year_min=2022" \
  --data-urlencode "page_size=200"
```

Then aggregate client-side:

```python theme={null}
from collections import Counter
venues = Counter()
quartile = {}
for paper in results:
    name = paper.get("journal_name")
    if not name:
        continue
    venues[name] += 1
    quartile[name] = paper.get("sjr_best_quartile")

for name, n in venues.most_common(15):
    print(f"{n:>3}  Q{quartile.get(name, '?')}  {name}")
```

Add `open_access=true` if your funder mandates OA, and `sjr_max=1` to see only the top-quartile subset — but look at the unfiltered list first, or you will miss the specialist venue that is the actual best fit.

## What to check before you trust it

* **Frequency is not acceptance probability.** The journal publishing the most papers in your area may also reject the most. Use the list to build a realistic ladder — a reach, two solid targets, a fallback.
* **Check scope pages by hand.** Journals shift scope, launch sections, and close them. The literature lags those changes by a year or more.
* **Watch for a topic sitting mostly in conferences.** In fast-moving computational fields the venue that matters may not be a journal at all, and journal-only tallies will mislead you.
* **Quartile is a blunt instrument.** SCImago quartiles vary by subject category; a Q2 in a narrow specialist category can be more selective than a Q1 in a broad one.

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