Word-entries and Big Data in <i>Lexicons of Early Modern English</i>
Bibliographic record
Abstract
This brief thirty-year history of Lexicons of Early Modern English, an online database of glossaries and dictionaries of the period, begins in a fourteenth-floor Robarts Library lab of the Centre for Computing and the Humanities at the University of Toronto in 1986. It was first published freely online in 1996 as the Early Modern English Dictionaries Database. Ten years later, in a seventh-floor lab also in the Robarts Library, it came out as LEME, thanks to support from TAPoR (Text Analysis Portal for Research) and the University of Toronto Press and Library. No other modern language has such a resource. The most important reason for the emergence, survival, and growth of LEME is that its contemporary lexicographers understood their language differently from how we, our many advantages notwithstanding, have conceived it over the past two centuries. Cette brève histoire des trente ans du Lexicons of Early Modern English, une base de données en ligne de glossaires et de dictionnaires de l’époque, commence en 1986 dans le laboratoire du Centre for Computing and the Humanities, au quatorzième étage de la bibliothèque Robarts de l’Université de Toronto. Cette base de données a été publiée gratuitement en ligne premièrement en 1996, sous le titre Early Modern English Dictionnaires Database. Dix ans plus tard, elle était publiée sous le sigle LEME, à partir du septième étage de la même bibliothèque Robarts, grâce au soutien du TAPoR (Text Analysis Portal for Research), de la bibliothèque et des presses de l’Université de Toronto. Aucune autre langue vivante ne dispose d’une telle ressource. La principale raison expliquant l’émergence, la survie et la croissance du LEME est que les lexicographes qui font l’objet du LEME comprenaient leur langue très différemment que nous la concevons depuis deux siècles, et ce nonobstant plusieurs de nos avantages.
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.
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 | 0.003 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.011 | 0.022 |
| Science and technology studies | 0.004 | 0.007 |
| Scholarly communication | 0.015 | 0.024 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 0.005 |
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".