‘Partially provided’: geography at the University of Toronto, 1844–1935
Bibliographic record
Abstract
In universities, as in everyday life, there is a fundamental need for geographical knowledge, even when no formal departments exist to provide instruction. This need was true in the University of Toronto during the decades before Griffith Taylor was appointed in 1935 to the first university Chair in geography in English‐speaking Canada. Using matriculation and annual university course examinations, university calendars and the papers of President Falconer and Professors James Mavor and Harold Innis, I trace the development of geography at the University of Toronto from the mid‐nineteenth century to the arrival of Taylor. Courses taught in selected aspects of physical and human geography in the Departments of Geology, Political Economy and History are particularly significant. Underlying this instruction, and also the desire to establish a geography department, was an acute awareness of the fundamental importance of geography to help understand a large regionally complex homeland, and a wider world .
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.009 |
| Science and technology studies | 0.008 | 0.012 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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; both teacher heads agree on what is shown here.
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".