Bibliographic record
Abstract
This is an accepted article with a DOI pre-assigned that is not yet published.Although ancient historians routinely create and exploit document corpora, and the notion of corpus is recognized as central in historiography, there has been little methodological focus on coming to a unified approach to the design and use of corpora. The massive expanse of digital information and processing capabilities over the past few years has also led to a diversity of approaches. After reviewing the history of the use of corpora in historiography, we examine how ancient historians have taken possession of digital practice, and how it has interacted with the notions and uses of textual corpora: there are many diverse and somewhat incompatible methodological perspectives on historical corpora. Next, we show how the textual corpus, as an input into historiography, cannot exist anymore as an object and must be seen as a process or a pipeline. Then, its multiple and sometimes opposite perceptions can be unified, at the same time making history more scientific in the sense of Lucien Febvre’s definition, for whom history is the scientifically elaborated narration of humankind’s activities.
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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.011 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.005 | 0.002 |
| Insufficient payload (model declined to judge) | 0.935 | 0.910 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".