Corpus Linguistics Strategies for Identifying Accepted Theories in Early Modern England
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.024 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.005 |
| Science and technology studies | 0.006 | 0.012 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.003 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it