Bibliographic record
Abstract
Creative geovisualization is situated at the intersection of geography, arts, and digital humanities with a particular emphasis on visualization and mapping that preserves, represents, and generates more authentic, contextual, and nuanced meanings of space and people with an artistic and humanistic perspective and approach. This is a creative expansion in critical GIS practices and a new alternative to traditionally science‐rooted approaches to GIS and mapping. Reflecting the experience of teaching a “creative geovisualization” course in an interdisciplinary curriculum, I demonstrate how critical and creative scholarship with mapping and geovisualization is introduced in the classroom and is illuminated in the students' creative practices. The class encompasses key epistemological and methodological groundings of creative geovisualization—including non‐representational theories; critical cartography and GIS; the convergence of geography, arts, and humanities; psychogeography; and qualitative and affective geovisualization. Empirical examples of students' works illustrate the blending of different modes of creative engagements with GIS and geovisualization and specific ways to work with various forms of embodied, relational, interpretive, and expressive geographies. GIS and mapping become creative as they continue evolving in process, and it is time that we deeply (re‐)imagine “the creative” in/of GIS in critical GIS pedagogies.
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.037 |
| Scholarly communication | 0.013 | 0.007 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 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".