Geography Geo-Wiki in the Classroom: Using Crowdsourcing to Enhance Geographical Teaching
Bibliographic record
Abstract
Geo-Wiki is a crowdsourcing tool used to derive information, based on satellite imagery, to validate and enhance global land cover. Around 5000 users are registered, who contribute to different campaigns to collect data across various domains (e.g., agriculture, biomass, human impact, etc.). However, seeing the Earth’s surface from above does not provide all of the necessary information for understanding what is happening on the ground. Instead, we need to enhance this experience with local knowledge or with additional information, such as geo-located photographs of surface features with annotation. The latest development in enhancing Geo-Wiki in this context has been achieved through collaboration with the University of Waterloo to set up a separate branch called Geography Geo-Wiki for use in undergraduate teaching. We provide the pedagogical objectives for this branch and describe two modules that we have introduced in first and third year Physical Geography classes. The majority of the feedback was positive and in, many cases, was part of what the student liked best about the course. Future plans include the development of additional assignments for the study of environmental processes using Geo-Wiki that would engage students in a manner that is very different from that of conventional teaching.
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.012 | 0.007 |
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".