Corpus Linguistics Strategies for Identifying Accepted Theories in Early Modern England
Bibliographic record
Abstract
The paper investigates the applicability of corpus linguistics to the construction of a database of intellectual history. Working with the Royal Society Corpus (RSC), it presents a series of corpus queries that can aid with computationally identifying potential instances of communal theory acceptance in England during the period of 1665-1800. These queries allowed to identify a set of noun-adjective pairs potentially synonymous with “accepted theory” and retrieve around 1,400 excerpts potentially indicative of instances of communal theory acceptance. The paper also discusses some strategies for identifying the epistemic agent, as well as the RSC’s place within the broader historical context. Finally, I argue that, in addition to exploring corpus linguistics strategies, methodologies for interpreting computationally retrieved data should also be developed.
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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 | 0.016 | 0.068 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.015 | 0.020 |
| Science and technology studies | 0.004 | 0.007 |
| Scholarly communication | 0.009 | 0.011 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".