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Record W2312940822 · doi:10.5026/jgeography.121.787

Trends of Geography in Canada

2012· article· en· W2312940822 on OpenAlexaffabout
Munetoshi Yamashita

Bibliographic record

VenueJournal of Geography (Chigaku Zasshi) · 2012
Typearticle
Languageen
FieldSocial Sciences
TopicHistorical Geography and Geographical Thought
Canadian institutionsCanadian Association of Geographers
Fundersnot available
KeywordsHuman geographyContext (archaeology)GeographySyllabusStrategic geographyHistorical geographyCultural geographyDistribution (mathematics)Regional scienceScale (ratio)Time geographyRegional geographyEconomic geographyFive themes of geographyAgricultural geographySocial scienceSociologyDevelopment geographyArchaeologyCartography

Abstract

fetched live from OpenAlex

This paper addresses the dynamic nature of geography in Canada today within the context of the history and recent trends of the Canadian Association of Geographers (CAG), activities of research groups in the CAG, changing patterns in the gender distribution of academic geographers, and regional distribution of geographers. CAG is an influential association of geographers, which plays an important role in defining fields of environmental studies and human geography. This paper examines the role of the association in the Canadian context. Furthermore, it outlines the geography education syllabus and research in the Department of Geography and Program in Planning at the University of Toronto, as well as some recent examples of successful research by Canadian geographers. All of these aspects make it clear that geography in Canada is closely related to the magnificent scale of the natural world and the diverse peoples occupying the land. Canada seems to have a national character that is distinct from the cultural and economic domination of the United States. While the interests and concerns of Canadian geographers had focused on East–West trends, growing attention is being paid to North–South trends and other aspects of Canadian geography.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.193
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.008
Science and technology studies0.0000.001
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.012
GPT teacher head0.256
Teacher spread0.244 · 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 teacher head, not a consensus.

Study designObservational
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

Citations0
Published2012
Admission routes2
Has abstractyes

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