Bibliographic record
Abstract
In this lesson, you will learn how to georeference historical maps so that they may be added to a GIS as a raster layer. Georeferencing is required for anyone who wants to accurately digitize data found on a paper map, and since historians work mostly in the realm of paper, georeferencing is one of our most commonly used tools. The technique uses a series of control points to give a two-dimensional object like a paper map the real world coordinates it needs to align with the three-dimensional features of the earth in GIS software (in Intro to Google Maps and Google Earth we saw an ‘overlay’ which is a Google Earth shortcut version of georeferencing). Georeferencing a historical map requires a knowledge of both the geography and the history of the place you are studying to ensure accuracy. The built and natural landscapes change over time, and it is important to confirm that the location of your control points — whether they be houses, intersections, or even towns — have remained constant. Entering control points in a GIS is easy, but behind the scenes, georeferencing uses complex transformation and compression processes. These are used to correct the distortions and inaccuracies found in many historical maps and stretch the maps so that they fit geographic coordinates. In cartography this is known as rubber-sheeting because it treats the map as if it were made of rubber and the control points as if they were tacks ‘pinning’ the historical document to a three dimensional surface like the globe. To offer some examples of georeferenced historical maps, we prepared some National Topographic Series maps hosted on the University of Toronto Map Library website courtesy of Marcel Fortin, and we overlaid them on a Google web map. Viewers can adjust the transparency with the slider bar on the top right, view the historical map as an overlay on terrain or satellite images, or click ‘Earth’ to switch into Google Earth mode and see 3D elevation and modern buildings (in Halifax and Dartmouth). Note: these historical images are large and will appear on the screen slowly, especially as you zoom into the Google map.
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 | 0.005 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.008 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.136 | 0.079 |
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".