Spatial Thinking, GIS, and Community Stories
Bibliographic record
Abstract
Geographic Information Systems (GIS) and maps are being used in exciting ways to engage communities around issues and to tell nuanced, complex, and even conflicting stories. This has the potential to shift power dynamics and shape new and enhanced knowledge of places and the people and experiences within those places. This talk will provide an introduction to GIS and spatial thinking through an overview of social and environmental justice applications of GIS in communities and examples of compelling mapping projects, in Vancouver and beyond. Looking to the future, ideas for using maps to shift perspective, communicate divergent experiences of place, and engage communities will also be discussed.\nSPEAKER BIO\nJulie Jones is a GIS & Map Librarian and Librarian for Geography at SFU. Previous to her role at SFU, she was a Liaison Librarian at McGill University (2010-2015) with a portfolio that included Geography, Urban Planning, and Social Work. From 2008 to 2010 she held concurrent positions as a librarian at Metro Vancouver's Harry Lash Library and the Fraser Valley Regional Library. Delivering GIS and data literacy workshops has been a part of Julie's work for several years, and it is something she is passionate about because it provides her with an opportunity to demystify powerful and useful—but sometimes intimidating—concepts and ideas. Developing the Library's geospatial data and map collections so that they meet the needs of researchers and the community is another exciting area of her work. Julie holds a Master of Library and Information Studies from McGill University and a BA in English Literature from UBC. She is interested in the ways spatial thinking and GIS can empower citizens, make complex data more accessible, and help us better understand each other. Her recent research has included climate change research syntheses and the examination of the role of librarians in systematic reviews.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.017 | 0.045 |
| Scholarly communication | 0.019 | 0.018 |
| Open science | 0.002 | 0.014 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.010 | 0.001 |
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".