Working with Historical Textual Data: Preliminary Results from Applying Survival Analysis to the Old English Poetic Corpus
Bibliographic record
Abstract
Survival analysis (SA), a statistical method traditionally employed in fields such as medicine(e.g., modeling patient outcomes based on treatment efficacy or risk factors), has only recentlybeen applied to linguistic research. Studies, such as Van de Velde and Keersmaekers’ (2020)evolutionary model of lexical longevity and Vogelsanger’s (2023) analysis of rhyming patterns,highlight its potential. However, its applications to the Old English poetic koiné remain virtuallyunexplored. By building on prior applications of SA in evolutionary linguistics, we aim to offer aquantitative framework to interrogate which linguistic features correlate to the obsolescence ofOld English poetic vocabulary over time. To this purpose, we apply SA to investigate arepresentative segment of the Old English poetic corpus, including the Vercelli Book (28.96%lexical density) the Nowell Codex (11.55%), the Exeter Book (23.57%), the Junius Manuscript(24.82%), and the Paris Psalter (18.94%).Our dataset consists of 182 words, of which 119 are classified as “dead” (i.e., no longerin use), yielding a survival rate of 34.62% and a death rate of 65.38%. Key variables, such asword frequency, z-score, syllable count, manuscript provenance, morphosyntactic roles, and thesemantic categories defined by the Historical Thesaurus of English are analyzed to identifypatterns in lexical survival. The results reveal strong correlations: syllable count (p = 0.03) andprovenance (p = 0.03) are significant predictors of word survival, while Historical Thesaurus ofEnglish categories show an even stronger correlation (p = 0.001). These findings suggest thatsemantic fields—particularly those related to the mind (e.g., emotion, cognition), society (e.g.,kinship, governance), and the world (e.g., nature, physicality)—play crucial roles in determiningthe longevity of Old English poetic vocabulary, likely due to their cultural resonance. Byadapting SA to this context, this study introduces a novel quantitative approach to understandinglinguistic change. It also extends the methodological scope of digital philology, addressingNichols and Altschul’s (2012) call to realize the field’s interdisciplinary aspirations.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.003 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".