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‘Partially provided’: geography at the University of Toronto, 1844–1935

2008· article· en· W2086306080 on OpenAlexafffundvenueabout
John Warkentin

Bibliographic record

VenueCanadian Geographies / Géographies canadiennes · 2008
Typearticle
Languageen
FieldSocial Sciences
TopicHistorical Geography and Geographical Thought
Canadian institutionsYork University
FundersUniversity of TorontoVictoria University
KeywordsMatriculationLanguage geographyHomelandHuman geographyRegional geographyPolitical geographyHistorical geographyFive themes of geographyTRACE (psycholinguistics)Everyday lifeUrban geographyGeographySociologyPoliticsMedia studiesSocial scienceMathematics educationPolitical scienceDevelopment geographyPsychologyUrban planningEngineering

Abstract

fetched live from OpenAlex

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.

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 imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.988
Threshold uncertainty score0.843

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.006
Science and technology studies0.0120.007
Scholarly communication0.0040.002
Open science0.0010.002
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0210.002

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.

Opus teacher head0.010
GPT teacher head0.195
Teacher spread0.185 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

Study designQualitative
Domainnot available
GenreEmpirical

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".

Quick stats

Citations3
Published2008
Admission routes4
Has abstractyes

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