Downtown Toronto’s emergent properties: Exploring new methods for using port records to disaggregate urban metabolism in Toronto, Ontario, 1850-1926
Bibliographic record
Abstract
Between 1850 and 1926 Ontario’s capital city, Toronto, grew from a small colonial port to one of the largest cities on the Great Lakes. In this article we introduce a rich time series dataset of ships entering the city’s port and the commodities they carried, explore its potential for urban metabolism research, and consider some of its limitations. We argue that the detail recorded in the ledgers of the Toronto Harbour Master affords multiple temporal and geospatial scales of analysis to study the city’s urban metabolism (e.g. seasonality and consignees of specific commodities), which means historians can use these quantitative sources to move beyond simplistic input-output evaluations and consider the nuance and complexity that characterized the role of the port in the city’s social, economic, and environmental history. We demonstrate that the port was vital to the process of assembling the city even as the railways became dominant in Ontario during the second half of the nineteenth century. As the city grew, the data reveal that the port remained an important node within a broader Great Lakes socioecological system at the same time as it served the city’s downtown and discrete subsystems of its urban metabolism.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.006 | 0.013 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".