Bibliographic record
Abstract
One of the recurring questions of world literary history is how to ensure that marginalized writers are represented. The advent of a data-driven literary history has made this question even more pressing, as collaborative and distributed projects like Wikidata have been shown to exhibit large gaps between groups, despite the diversity of topics and contributors represented. In order to get an idea of how entrenched the gender gap is within literary Wikidata, I will examine the representation of male writers versus writers who are women or other genders using Wikidata. Since the data are vast and complex, I will particularly focus on the subset that is related to French and Francophone writers in Wikidata with an eye to how the gender gap evolves across nations, geography, and time. I will show that the gender gap is less significant in recent periods and in smaller Wikidata communities and that the largest Wikidata communities with the longest histories have larger gender gaps. As in other subject fields, literary topics in Wikidata are disproportionately linked to male authors. Finally, I consider some ways that the gender gap intersects with linguistic justice movements and how the gender gap can be reduced in literary Wikidata. The patterns in the data and procedure may be generalizable to literary Wikidata as a whole, especially larger Wikidata communities, because the gender gap in both the French and the Francophone subsets of the data is close to the global average; there is also a higher-than-average representation of writers of other genders that resembles other large Wikidata communities.
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 | 0.008 | 0.047 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.009 | 0.011 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".