"Could I but Mark Out My Own Map of Life": Educated Women Embracing Cartography in the Nineteenth-Century American South
Bibliographic record
Abstract
In the early 1870s, instead of writing a story, drawing a picture, or composing a poem, William Henry Bailey made a hand-drawn map for his ailing mother. Despite its lack of beauty, its contents betray its unique and poignant intent: the map shows Hillsborough, NC, as it might have appeared in 1839, when Priscilla Bailey was a young wife and mother, surrounded by friends and social activities. Decades later, when Priscilla was living far from all that and recovering from a bad fall, her son created his map, with its narrative explanation of every detail, to comfort his mother by reminding her of happier days and treasured connections. Why did Bailey - a lawyer with no particular expertise in drafting, surveying, or drawing - decide to make a map to stir healing memories in his mother? The answer may lie in an evocative cartographic culture among elite Southern women of her generation. Educated to use maps, engaged daily with graphic information in their sewing and needlework patterns, and frequently separated from kin and other loved ones, these women turned to the cartographic form with familiarity and an expectation of solace. They used geographic vocabularies and metaphors to express their sense of isolation and their need for connection, even as gender norms and physical realities limited their travel and restricted them to their homes. In this article, the practice and implications of this emotional cartographic culture are explored through the examples of two women, Priscilla Bailey and Ellen Mordecai; this exploration is intended to demonstrate the value of a manuscript-based, biographical approach to the history of domestic map use.
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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.024 | 0.017 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.005 |
| 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".