Illuminating spaces in the classroom with qualitative GIS
Bibliographic record
Abstract
As social and postmodern ontologies continue to shape our definition of space, undergraduate instructors have struggled to incorporate these paradigms in the geography classroom. Recent research suggests that practical applications using field work, qualitative research, and geographic information science can augment students’ understanding of these spatial ontologies. Qualitative GIS holds promise as a means to integrate these methods in geographic education, yet there are no signs to date that the methodology has transitioned from research to teaching. This paper details our attempt to incorporate qualitative GIS into an undergraduate urban field studies course in lieu of a strictly lab-based GIS assignment. We outline our approach before discussing students’ engagement with the assignment in greater depth. Drawing from field experiences and deliverables across four terms, we argue that teaching from a qualitative GIS framework can effectively communicate the fundamentals of modern spatial theory and geographic research methods to students as they investigate problems in the field. We also note recurring challenges to mixed-methods teaching for students unfamiliar with the new methods presented. We close by discussing avenues for instructors in different circumstances, e.g. personal skills sets and class characteristics, to consider qualitative GIS in their classrooms.
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How this classification was reachedexpand
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 | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".