Teaching Cartography in Academia: A Historical Reflection and Discussion of a 2007 Survey of Canadian Universities
Bibliographic record
Abstract
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.
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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 | 0.014 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.008 | 0.030 |
| Science and technology studies | 0.042 | 0.011 |
| Scholarly communication | 0.014 | 0.004 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 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; 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".