Bibliographic record
Abstract
ABSTRACT Pennsylvania is often regarded in the historiography of public health, medicine, urban, and industrial history as little more than a disaster; her chief cities, Philadelphia and Pittsburgh, highlighted the health disparity between rich and poor, their tenements among the most degraded in the nation, their water poisonous, and their skies leaden. The plight of the state's city dwellers were exceeded in misery and mortality rates only by the wretched conditions of the coal patches, small steel towns, and timber camps that dotted the Commonwealth. By the turn of the twentieth century enough political will was mustered to overcome objections to a state department of health. Benjamin Franklin Royer emerged from the public health apparatus of Philadelphia to assume a critical role in the department, eventually rising to its head and guiding the state through the influenza pandemic of 1918. In the early 1920s, after a titanic explosion leveled most of Halifax, Nova Scotia, Royer took the lessons he learned in the state and rebuilt Halifax, starting the first public health nursing program in Canada. Between 1926 and 1932 he was medical director of the National Society for the Prevention of Blindness where he led a campaign against bacteria-induced blindness in newborns and adolescents before returning to Pennsylvania to work on antiblindness and tuberculosis control efforts.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.117 | 0.055 |
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".