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A Thought Too Far: A Case for a Corpus Approach to Bad Knowledge in Old English Literature

Paper · 2025 · Rían Boyle

Summary

Boyle asks how early English writers understood knowledge that was morally wrong. The paper calls this "Bad Knowledge." No Old English text sets out an explicit theory of knowledge, so close reading of a few works cannot answer the question. The paper instead uses a "distant reading" pilot study over all of surviving Old English, taken from the Dictionary of Old English Corpus. The method has four steps:

  • build a knowledge-related lexicon from the Thesaurus of Old English;
  • search the corpus and extract matching sentences;
  • attach metadata such as manuscript date;
  • analyse the hits both by counting and by close reading.

About two hundred sentences from 97 texts were sorted by hand into thematic categories. The data are too few for firm claims, but one pattern recurs: Bad Knowledge goes with rejecting authority and order, whether that order is divine, royal or the bond between teacher and pupil. The study matters mainly as a proof of concept. The same pipeline could trace any cultural concept across the whole Old English corpus.

Key points

  • The starting puzzle is Ælfric's Be Þam Halgan Gastes, which renders Latin scientia as god ingehyd ("good knowledge"). Only 6 of roughly 130 Old English glosses of scientia use that wording, which implies a contrasting bad kind of knowledge.
  • The lexical definition began from 642 knowledge-related words in the Thesaurus of Old English. These were then narrowed to terms carrying a negative moral sense.
  • 175 occurrences were close-read and assigned to five categories: Degradation, Influence of Satan/Devils, Poor Leadership or Guidance, Maintenance of Order, and Blindness to God.
  • Counts by manuscript date show Degradation appearing steadily across periods. Influence of Satan/Devils, by contrast, clusters in a mid-eleventh-century spike.
  • The author treats the dataset as too small for strong conclusions. Widening the term list is proposed as the next step.

Interesting ideas

  • I1. Define a vague concept by first harvesting its whole semantic field from a thesaurus and then narrowing to the evaluative subset; this gives a reproducible search lexicon instead of a hand-picked handful of keywords.
  • I2. A marked evaluative modifier (Ælfric's "good" knowledge) signals that the bare noun has a contrasting, negative counterpart; the unusual choice itself carries meaning.
  • I3. Count how one source word is rendered across many parallel glosses, then flag the rare renderings as likely deliberate interpretive decisions.
  • I4. Pair exhaustive retrieval with manual thematic categorisation, then cross the categories with metadata such as date to see which themes are stable and which spike.
  • I5. Read the zero cells in a category-by-period table as evidence: a category with few empty bins is a constant theme, while one concentrated in a single bin is episodic.

For Mova

How these ideas could serve Mova, our small open language model — its files, stages and tests:

  • I1 (world): A concept like "deception" or "harm" could be compiled from WordNet/FrameNet neighbourhoods into a lexicon, then used to find every mention in a text's world.
  • I2 (frames): The implicature module could treat rare evaluative modifiers on normally unmodified nouns as cues that a contrast class is being evoked, and record that alternative as an implied proposition.
  • I3 (back): Translation through the meta-world could keep distributions of renderings per source concept, so it can tell a default choice from a marked one and preserve that markedness.
  • I4 (learn): Error analysis could categorise failures by hand and cross them with metadata like genre or source treebank, separating systematic weaknesses from local bursts.
  • I5 (learn): In evaluation, empty or near-empty slices per phenomenon and per data source can reveal brittle coverage that an aggregate score hides.

Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.

 Institutions

🎓Trinity College DublinDublin · Ireland24 works · 1,231 citations

 Fields, topics and concepts

📚 Shelves 3The field of science this work belongs to (OpenAlex fields).Arts and Humanities · 16,039 worksLiterature and Literary Theory · 519 worksClassics · 116 works

🧭 Topics 2Research subjects and languages the work is about.English · 2,263 worksHistorical linguistics & language change · 249 works

Cite

Cite as Rían Boyle. 2025. A Thought Too Far: A Case for a Corpus Approach to Bad Knowledge in Old English Literature. SELIM 30.

The paper itself, not this page. BibTeX from the library's record of the work.

@inproceedings{boyle2025thought,
  title = {{A Thought Too Far: A Case for a Corpus Approach to Bad Knowledge in Old English Literature}},
  author = {Rían Boyle},
  booktitle = {SELIM 30},
  year = {2025},
  doi = {10.17811/selim.30.2025.97-123},
  url = {https://doi.org/10.17811/selim.30.2025.97-123}
}

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