Measuring U.S. 19th Century Economic Activity Using Unexploited Railway and Postal Micro-level Data
Bibliographic record
Abstract
For the past several years, we have presented and published studies based on postal related data, from postmaster cash books and the Official Register, where we use postmaster salary data as a measure of local, highly disaggregate proxies for general economic activity at town and village level. Using micro-level, high frequency, nationally uniform and previously unknown data, we will report on the outcome of measuring levels of economic activity, political influence and social mobility phenomena. In our latest work, we will use a recently published work of railroad history investments in the 19th century. The railroad history we have is highly detailed, naming particular towns and routes. Our own micro data will allow us to associate our postmaster data with railway town information at the same micro level. Our data will also allow us to report the economic activity of non-railway towns. We will then have, at the micro-level, bi-annual comparisons made over the life of the railway routes. The relative economic, political and demographic impact of railway investment will be examined. For example, as we have the names, birthplaces and ethnic origins of postmasters in addition to their salaries. We can measure not just differences in economic activity between railway and non-railway towns but even examine questions like: "Are the railway towns places where new immigrants get to be postmasters more quickly than elsewhere?" Our larger purpose is to advertise our ever-expanding postal based dataset, which provides information of interest to economists, sociologists, historians and political scientists.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.011 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".