Metadata-Version: 2.5
Name: stopslop
Version: 0.1.1
Summary: Measure a paper's prose against how a venue actually wrote, before and after LLMs existed
Project-URL: Homepage, https://github.com/dmitrii-khizbullin/stopslop
Project-URL: Source, https://github.com/dmitrii-khizbullin/stopslop
Author: Dmitrii Khizbullin
License: MIT License
        
        Copyright (c) 2026 Dmitrii Khizbullin
        
        Permission is hereby granted, free of charge, to any person obtaining a copy
        of this software and associated documentation files (the "Software"), to deal
        in the Software without restriction, including without limitation the rights
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        copies of the Software, and to permit persons to whom the Software is
        furnished to do so, subject to the following conditions:
        
        The above copyright notice and this permission notice shall be included in all
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        THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
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        SOFTWARE.
License-File: LICENSE
Keywords: academic-writing,corpus,llm,prose,stylometry
Classifier: Development Status :: 3 - Alpha
Classifier: Intended Audience :: Science/Research
Classifier: License :: OSI Approved :: MIT License
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3 :: Only
Classifier: Topic :: Scientific/Engineering
Classifier: Topic :: Text Processing :: Linguistic
Requires-Python: >=3.9
Requires-Dist: pypdf>=5
Provides-Extra: pdfminer
Requires-Dist: pdfminer-six>=20240706; extra == 'pdfminer'
Provides-Extra: pymupdf
Requires-Dist: pymupdf>=1.24.3; extra == 'pymupdf'
Provides-Extra: pypdfium2
Requires-Dist: pypdfium2>=4; extra == 'pypdfium2'
Description-Content-Type: text/markdown

# 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.

```bash
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:

```bash
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.

| extra | licence | speed | notes |
|---|---|---|---|
| *(default)* `pypdf` | BSD-3-Clause | 0.82 s/paper | pure Python, installs anywhere |
| `[pymupdf]` | **AGPL-3.0** or commercial | 0.12 s/paper | best quality; opt-in, never a default |
| `[pypdfium2]` | BSD-3 / Apache-2.0 | **0.10 s/paper** | fastest; platform binary wheels |
| `[pdfminer]` | MIT | 2.13 s/paper | slowest |

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

```bash
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.
