The history and development of the theory and practice of cybercartography
Bibliographic record
Abstract
This paper describes the development of cybercartography since the introduction of the term in 1997. Although the origins of cybercartography were largely conceptual in nature, the evolution of cybercartography to date has been an iterative process reflecting the creative interplay between theory and practice. A major step forward was made in 2002 when the Geomatics and Cartographic Research Centre at Carleton University received a $2.5 million grant from the Social Sciences and Humanities Research Council of Canada to explore the utility of cybercartography to what was described as the New Economy. By 2006, the interaction between theory and practice had led to considerable advances in cybercartography as a holistic, location-based concept and two new cybercartographic products, the Cybercartographic Atlas of Antarctica and the Cybercartographic Atlas of Canada's Trade with the World, were produced. Between 2006 and 2009, cybercartography was further developed as a result of interaction with indigenous communities, especially in Canada's north and new interactive atlases such as the Kitikmeot Place Names Atlas and the Community Atlas of Arctic Bay were created in cooperation with the communities involved. The Nunaliit Cybercartographic Atlas Framework, built using open source software and open specifications and standards, was developed to facilitate direct input to these atlases. Cybercartography is now entering a new phase in both theory and practice building on a recently completed prototype atlas of Indigenous Perspectives and Knowledge.
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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 | 0.007 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.007 | 0.077 |
| Scholarly communication | 0.014 | 0.010 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".