Bibliographic record
Abstract
For the past twenty years, early modern scholars have called for more scholarly attention to people and places outside of Europe. An impressive increase in literary research on non-European texts has resulted, and I describe positive aspects of this trend, using the MLA International Bibliography database. However, research on African-language literatures has declined since 2003 or has continued to flatline at nothing. A radical antiracist solution is needed, for no field can succeed with Africa as a lacuna. I call on all early modern scholars, regardless of their language knowledge, to cite at least one early modern African-language text in their next publication. I describe five such in this article, a tiny sample of the thousands of written texts that Black Africans across the continent composed in African languages before 1830. Asking early modern scholars to embrace the uncomfortable practice of “token citation” will enable these texts to circulate in the realm of knowledge and further efforts to diversify and broaden the field.
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 | 0.005 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.009 | 0.008 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.009 | 0.014 |
| Insufficient payload (model declined to judge) | 0.051 | 0.026 |
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".