Visualizing Kitchener: Geocoding Historical Street Information
Bibliographic record
Abstract
When studying and investigating the history of a property or neighbourhood, common resources often sought by researchers include air photos, fire insurance plans and historical maps. These will provide information about what an area looked like, often offering the building’s footprint and surrounding landscape. Timespan studies will no doubt show growth, development and possibly changes to the buildings of interest. But what the rich resources don’t tell the researcher is information about the people connected to those buildings. Who lived or worked there? What did they do for a living? Did they move often? Did they change jobs regularly? These types of questions can’t be answered with just maps alone as they require a detailed census to go along with it. To fill this type of need, Geospatial Centre staff at the University of Waterloo Library embarked on a massive-scale digitization and geo-location city directory project – one that had started in 2019, has involved at least 40 staff members, and still has a couple years to go before completion. This paper will summarize the project thus far, with a focus on the journey of geocoding historical streets.
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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 | 0.002 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.012 | 0.020 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.006 |
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".