Bibliographic record
Abstract
How are historic texts selected for digitization in mass digital archives? It can be surprisingly challenging to know what is in the databases which now implicitly structure nearly all literary research. These resources, therefore, may operate as “black boxes,” complex equipment whose operations are mysterious to users. Examining the specific institutional histories of four literary databases, I explore how the scholarly practice of historical recovery might adapt to the site of the database. I measure and historicize uneven digitization as it might affect women writers of the late eighteenth century, to shed light on broader processes of textual selection. Specifically, I examine the English Short Title Catalogue (ESTC), Eighteenth Century Collections Online (ECCO), the Text Creation Partnership (TCP), and HathiTrust. I describe the development of these databases from the 1970s to the present. I then compare each database’s holdings of works published in England 1789–99, manually identifying how many works are attributed to women. I find that these databases in fact marginalize “authorless” works, namely, those which are unsigned, attributed to unidentified pseudonyms, or written by corporate authors. To bridge literary and archival understandings of textual value, I present a definition of value as a principle of selection, arguing that a site of textual selection is also a site of textual valuation.
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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.009 | 0.020 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.009 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.009 | 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".