ECONOMY: CANADA’S TRADE WITH THE WORLD AND THE CYBERCARTOGRAPHIC ATLAS OF ANTARCTICA.
Bibliographic record
Abstract
Cartography and geographic information science will provide a meaningful contribution to the sustainable future development of our world. However, to realize this goal of contributing to sustainable future development, cartographers and geographic information scientists must take a holistic, critical approach to research that considers not only technological effectiveness but also the societal context in which this research is being carried out. The Geomatics and Cartographic Research Centre at Carleton University has been awarded a four year, major collaborative research grant. The funded research project titled Cybercartography and the New Economy will work within the conceptual framework of cybercartography. A multidisciplinary research team aims to contribute to knowledge in areas described by cybercartography. Brief overviews of two products being developed as part of the CNE applied research project are presented. The products are being developed with a user-centred approach to design. This is expected to result in improved products and the creation of new knowledge and methodologies that can support further development of cybercartography. While each product will be produced independently and for different user groups, the research will be carried out in an integrated manner. Effective integration of researchers from many disciplines may be challenging, however new insights possible through this multi-disciplinary perspective may well be on of the most valuable results of the research.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.006 | 0.017 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.040 | 0.004 |
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".