Familial Places in Jim Crow Spaces: Kinship, Demography, and the Color Line in William Faulkner’s Yoknapatawpha County
Bibliographic record
Abstract
Hosted out of the University of Virginia, Digital Yoknapatawpha is an international collaboration between scholars of William Faulkner and technologists at the Institute for Advanced Technology in the Humanities. The project team has encoded all the locations, characters, and events in Faulkner’s Yoknapatawpha fictions into a relational database that powers an open-access web-portal. Users can avail of an atlas of “deep-maps,” data visualizations, archival material, and aural and visual resources to explore, teach, and research his works. Using techniques common in ecology and demography, this paper leverages the data to investigate the relationship between race, kinship, and space. It concludes, tentatively, that the social world of Yoknapatawpha is far more rigidly bounded along racial lines than current scholarship suggests. In particular, most interactions between characters from different races happen in a familial context, and are the result of racialized labor exploitation or outright enslavement of African-American families by Anglo-Americans. The lack of interactions outside of this context underscores just how little agency non-white characters have in Faulkner’s fiction.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.012 | 0.006 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".