Rethinking GIS teaching to bridge the gap between technical skills and geographic knowledge
Bibliographic record
Abstract
Teaching GIS in universities, over the last few decades, has often been applied in focus. Yet academic \nresearch is much more than application: epistemology, representation, critical GIS have been gaining an \nincreasing share of research. This trend is paralleled by increasing awareness and sophistication in the \nprofessional practice of GIS. Nonetheless, the increasing availability of spatial analytical techniques in \ncommercial and freeware GIScience software, not paralleled by an increased knowledge in GIScience \npractitioners, raises questions about the maturity of the GIScience user community and the potential \nconsequences of an incautious popularization. Appealing to the average GIScience user by means of \nfriendly interfaces, most analytical functions fail to keep a standard promise of GIScience software: guiding \nthe user through a safe path to a successful application. This lack of guidance is perceived as a gap, the \nconsequences of which range from discouragement to naïve or incorrect applications. Future GIScience \nprofessionals should be prepared to look beyond their software interface, and the discipline should strive to \nmaintain its own rules and make its own decisions when it comes to packaging their tools. A key role can \nand must by played by those who teach GIS in our universities, whose task id to form a generation of \nGIScientists, not simply of GIS technicians.
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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 | 0.029 | 0.039 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.007 | 0.024 |
| Scholarly communication | 0.018 | 0.021 |
| Open science | 0.005 | 0.021 |
| Research integrity | 0.005 | 0.013 |
| Insufficient payload (model declined to judge) | 0.012 | 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".