Seeing through the fog: Digital problems and solutions for studying ancient women
Bibliographic record
Abstract
In spite of the proliferation of online resources dedicated to the study of the ancient world, there is nonetheless room for the improvement and expansion of methodology and content. This paper identifies two predominant problems in the realm of digital classics: the perpetuation of traditional methods of presenting research rather than the promotion of technology-driven analysis, and the virtual invisibility of ancient women in cyberspace. Arguing that there is a gender imbalance in Web-based resources for antiquity, two solutions are proposed beginning with the addition of more material regarding ancient women to existing platforms in the interest of equalization. Using an analogous project from McGill University as inspiration, an approach that combines ancient data with GIS analysis is proposed in order to make room for technology-driven research while beginning to mitigate the invisibility of women in the ancient world and on the Web.
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 | 0.018 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.008 | 0.009 |
| Science and technology studies | 0.017 | 0.051 |
| Scholarly communication | 0.021 | 0.038 |
| Open science | 0.004 | 0.019 |
| Research integrity | 0.008 | 0.008 |
| Insufficient payload (model declined to judge) | 0.010 | 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".