stopslop

Measure a paper's prose against how a venue actually writes — before and after LLMs existed.

Two baselines, always both: fossil (published up to 2022, so it cannot have been machine-assisted) and current (the last full year plus this one). The fossil window is the target; the current window is context, never an excuse.

It measures and locates. It never edits your paper: a tool that strips tells on the author's behalf produces text that passes a detector and still reads as machine-written, so check reports where the prose sits and locate gives the character offsets, and the writing stays yours.

Install

The wheel carries the baselines for all seven venues — 105 venue-years of measurements — so there is nothing to crawl and nothing to configure.

pip install stopslop              # pypdf, BSD-3-Clause, no copyleft
pip install "stopslop[pymupdf]"   # adds PyMuPDF (AGPL-3.0, opt-in)
pip install -e .                  # editable, for hacking on it

Then, from any directory, in any project:

stopslop check paper.pdf --venue tmlr --out report.md

No system tools required. Everything else is stdlib re and statistics.

Extractors

The extractor is part of the measurement. Across 24 papers the four supported extractors agree to within ~1% on prose metrics — first person 18.1–18.3, -ly adverbs 13.2–13.4, passives 10.3–10.5 — and disagree sharply on layout-sensitive ones: numerals runs 43.4 under pymupdf against 61.4 under pypdf. So measurements are stored per extractor and never mixed, and stopslop check refuses rather than compare across them.

Baselines for all of them ship in the base wheel, so an extra only decides what you can measure with, never what you can read. Pick one with --extractor or $STOPSLOP_EXTRACTOR.

pip install stopslop carries no copyleft: PyMuPDF is AGPL and is opt-in, so taking on that licence is always your deliberate choice.

Use

stopslop check paper.pdf --venue tmlr --out reports/paper.md   # score one paper
stopslop trend tmlr --out reports/tmlr_trend.md                # one venue's drift
stopslop crosstrend --out reports/total_trend_points.md        # venues kept apart
stopslop pooltrend  --out reports/total_trend.md               # venues pooled
stopslop stats tmlr 2022                                       # one crawled year
stopslop crawl tmlr 2022 2025 2026 --limit 20                  # fetch and measure

crawl is the only command that touches the network, and it needs no key or account. Everything else is offline.

Where the baselines come from

Resolved at call time, most specific first:

1. --root DIR on any command
2. $STOPSLOP_MEASUREMENTS
3. ./data in the working directory, if it exists
4. the measurements bundled in the package

So a project that measures its own papers into ./data uses them automatically, and everyone else gets the shipped ones. PDFs are cached separately, under --pdf-root / $STOPSLOP_PDFS / ./corpus.

Plots

--plot renders the trend figures and needs matplotlib, which is deliberately not a dependency and not an extra: the measurement path has no use for it. If you want figures, install it yourself.

Scope

Seven venues today — TMLR, NeurIPS, ICML, ICLR, ACL, EMNLP and JMLR — each measured per year, with the fossil window reaching back to 2010 where the papers exist. A venue is one entry in a registry, and stopslop crawl builds any of them from scratch, so a corpus of your own is a command rather than a fork.

Every number is auditable. Each report prints the baseline it used, the extractor, the date, and the n behind each window, and the same measurements sit in plain JSON inside the package. locate gives the character offsets behind a rate, so a claim about your prose can be checked against the sentence that produced it.

It compares prose to a corpus. It does not classify authorship. A metric outside the fossil range is a question about a sentence, not a verdict about a writer — academic English is written well in many registers, and this tool measures distance from one venue's historical distribution, nothing more.

MIT licensed.