Mueller, Martin, and Joseph Loewenstein, co-PIs. EarlyPrint: Curating and Exploring Early Printed English
Bibliographic record
Abstract
EarlyPrint is an exciting new venture designed to supplement the digital resources provided by the Early English Books Online Text Creation Partnership (EEBO-TCP).In some ways, I am an out-of-left-field choice of reviewer, as I am generally bemused by technology.In other ways, I am an obvious choice: if such resources are of use to someone of my limited IT skills, they must be genuinely valuable.The attractive site provides the user with two options: to read and edit texts (the EarlyPrint Library), or to search and analyze texts (the EarlyPrint Lab).Clicking on the first option takes the user to a page with instructions on how to edit the texts and become a co-curator.There is also a link that redirects you to the "Texts" tab, which is where you will find the site's engine to search the EEBO-TCP archive.Here, one can search the texts, much like on the EEBO-TCP site (quod.lib.umich.edu/e/eebogroup).To test it out, I brought up the works of Thomas Nashe and Edmund Spenser and then performed a keyword search, satisfying myself that "Braggadocio" was indeed a Spenser coinage, and that the word "dildo" was a refrain in a song, could mean a fool, and took on its more common meaning in the later 1590s after Nashe's pornographic poem, A Choice of Valentines, which I have been editing (honestly).I tried the word "hint, " as I have written on that, and the results were similarly rapid, bringing up the expected mixture of terms that meant "blow" and those that meant "suggestion." The search engine did seem to be impressively fast and is certainly much quicker than the one supplied by Historical Texts (historicaltexts.jisc. ac.uk), which verges on the unusable, and a bit swifter than that accompanying EEBO.The "search and analyze" button takes the user to a more complicated screen with four further options: "Catalog Search, " "Corpus Search, " "Discovery Engine, " and "Download Texts and Metadata." Beneath this there is a section marked "Visualizations" with four more buttons: "N-gram Viewer, " "Bibliographia, " "Books per Year, " and "Word Counts over Time." The "Corpus Search" seems to duplicate the searches I carried out under the "Texts" tab, with
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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.016 | 0.008 |
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".