Bibliographic record
Abstract
Armed with Irish national school educations that provided skills in needlework, cooking, fine laundry, good spelling, and nice penmanship, young single women emigrated to America when post-famine Ireland had little to offer them. Prepared to seize the best opportunities their school-learned skills afforded, they found servant positions in upper-class homes. Once married—usually to a countryman—they encouraged their daughters to make the most of their own opportunities by becoming teachers. Sons could go early into the work force; daughters stayed in high school and went to normal school. This generation of Irish American women became a substantial proportion of urban public school teachers, and the girls' accomplishments became family advances. The mothers had been servants of the rich. The daughters became “servants of the poor.” Janet Nolan chronicles women who followed a path from the national schools of Ireland to the public schools of Boston, San Francisco, and Chicago in the half century surrounding the turn of the twentieth century. The cities had differences, but each had a large Irish population, and in all three Irish American women were the largest single ethnic group among public school teachers. In Boston, they were one-quarter of the teachers by 1908; in San Francisco, almost half by 1910; in Chicago, they were perhaps 70 percent in 1920.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.244 | 0.132 |
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".