Aaron's Solution, Instructor's Problem: Teaching Surface Analysis Using GIS
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract Abstract Teaching GIS is relatively simple, a matter of helping students develop familiarity with the software. Mapping as an aid to thinking is harder to instruct. This article presents a laboratory and lecture package developed to teach the utility of mapping in a course on spatial data analysis. Following a historical review of the use of surface mapping in medicine, it investigates a recent outbreak of salmonella. The lab teaches two different analytic techniques, hierarchical buffers and a surface kerneling approach. The result is a historically grounded program in which different means of address are compared and considered by students. Key words: choleradensityGISsurface analysissalmonella Acknowledgments The authors wish to express thanks to the peer reviewers who carefully reviewed an earlier draft of this article. Ken Denike, Ph.D. is professor emeritus of geography at theUniversity of BritishColumbia and serves as chairperson of the Vancouver School Board, Vancouver, Canada. He was recipient of the 2004 National Council for Geographic Education award for the best article related to teaching in the university/ college. Tom Koch, Ph.D. is adjunct professor of medical geography at the University of British Columbia and adjunct professor of gerontology at Simon Fraser University. He is the author of fourteen books, including Cartographies of Disease (2005). Notes Note 1. The intersects can be either manually constructed, or in some programs computed automatically. An advantage of this approach is that it insists students see that manual approaches are possible and the GIS simply facilitates the process whose rationale is clear.
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.
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it