Deep mapping in geography education: learning about a distant place and people in a summer school as a ‘committed outlier’
Bibliographic record
Abstract
This article discusses Deep Mapping in Geography teaching and learning by drawing on a case study of a summer school organised during the COVID-19 pandemic. Deep Mapping was used to foster deep learning among the students and teach them about a distant place and people. The exercise tasked the students to work on the creation of layered maps representing the fieldwork site, the city of Vancouver, Canada. Critical student reflections about the Deep Mapping process are used to address some of the benefits and challenges. The Deep Mapping exercise stimulated the students to critically engage with the diverse summer school materials, move beyond a superficial view of the city, maps and mapping, and reflect on their positionality. The method is promising in light of making deep engagement with other places more accessible to those who might not have or be inclined to access such international educational experience and also offers another opportunity for blended learning. In conclusion, we argue that Deep Mapping offers a timely and highly engaging approach to learn about a place and people from another part of the world – be it on location or at a distance.
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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 | 0.005 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.016 | 0.022 |
| Scholarly communication | 0.010 | 0.013 |
| Open science | 0.003 | 0.026 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".