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Record W2116608160 · doi:10.3138/carto.45.1.5

Teaching Cartography in Academia: A Historical Reflection and Discussion of a 2007 Survey of Canadian Universities

2010· article· en· W2116608160 on OpenAlexaffvenueabout
Sally Hermansen

Bibliographic record

VenueCartographica The International Journal for Geographic Information and Geovisualization · 2010
Typearticle
Languageen
FieldSocial Sciences
TopicGeographic Information Systems Studies
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsGeovisualizationRepresentation (politics)GeographyCartographyVisualizationLibrary scienceVisual artsData scienceInformation visualizationEngineeringComputer scienceArtPolitical science

Abstract

fetched live from OpenAlex

Courses in cartography that thrived in university departments of geography in the 1970s and 1980s declined in number in the early 1990s, mostly to accommodate GIS but also partly in response to the cultural turn in geography away from quantitative methods. Today, we are witnessing a revival of mapping and cartography as a result of enhanced software tools for the creation of maps, the Internet and public mapping sites for the creation and dissemination of maps, community cartography projects, and a shift from traditional cartography to representation and geovisualization. As described in this article, a 2007 survey of cartography course offerings at Canadian universities sought to explore whether, and how, this revival has been reflected in the academic teaching of cartography necessary to support aesthetics of map design and tools for geovisualization. The results demonstrate that cartography courses are offered at almost all Canadian universities. At the introductory level, course content does not vary significantly from fundamental principles of cartography. At the advanced level, however, course content is highly varied, embracing the wide range of topics relevant to the new cartography and visualization epistemology of today.

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)0.004
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.318
Threshold uncertainty score0.728

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0040.001
Science and technology studies0.0010.000
Scholarly communication0.0000.001
Open science0.0000.000
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 head0.022
GPT teacher head0.322
Teacher spread0.300 · 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.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations3
Published2010
Admission routes3
Has abstractyes

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