ROBERTA M. STYRAN and ROBERT R. TAYLOR. This Great National Object: Building the Nineteenth-Century Welland Canals.
Bibliographic record
Abstract
Since the late 1970s Roberta M. Styran and Robert R. Taylor have worked in heritage groups and produced several books concerning elements of the history of the Welland Canal, which finally crossed the Niagara peninsula in 1833 to link Lake Ontario with Lake Erie. In This Great National Object: Building the Nineteenth-Century Welland Canals, the authors depart from a traditional organization that separates the building of the three canals—the first in 1824–1833, the second in 1840–1845, and the third in 1871–1882. Instead, they present a series of longitudinal studies of discrete components of construction such as route selection, locks, and water control, as well as the more expected categories of management and labor. The uninterrupted account of each element through the building of the three works facilitates juxtaposition of images of plans, technical drawings, the occasional “landscape,” and, for the third canal, photographs that, alongside careful explication, illuminate sweeping changes that seemed piecemeal to contemporaries. For example, over the course of sixty years, canal alignment shifted markedly toward the Niagara River. While the number of locks decreased from forty to twenty-six, the size of each, now built with masonry rather than wood, almost tripled.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".