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Limitations & authorship

slopscore is not an AI detector

It flags writing patterns, not provenance. A high score means the text is dense with formulaic, generic, low-specificity, over-polished patterns, which occur in low-effort AI output and in plenty of human writing (marketing copy, SEO, fan fiction). It scores text, not authors, and must never be used to accuse anyone.

Known limitations

  • Non-native English / plain writing is over-flagged by pattern detectors (one study found ~61% false positives on non-native essays). slopscore mitigates with a corroboration gate, a negative human-writing signal, and abstention, but residual risk remains.
  • Short text (< ~300 words) is unreliable; under ~100 words the label abstains.
  • Genre matters: marketing and travel prose naturally resemble slop. Use --profile.
  • Light paraphrasing evades pattern matching, as it does all detectors.

See the project MODEL_CARD.md for measured numbers and the real-corpus (MAGE) experiment.

Authorship signal (optional, separate, caveated)

slopscore ships no authorship detector. Every candidate (Binoculars, Fast-DetectGPT, GLTR) collapses on paraphrase, is biased against non-native English, or is academic-only; watermark detection covers almost no models. What it offers instead is a pluggable adapter (slopscore.detectors.AuthorshipDetector) behind the [detectors] extra: if you bring your own detector, its result is reported in a separate field with a mandatory caveat and is never folded into the SlopScore. slopscore-lint scan --detector reference wires a no-op example to show the separation. This is for curiosity, not accusation.