Counter-Mapping: Visual Strategies for Alternative Imaginaries
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Throughout the pandemic, maps of visual data published in the digital mediascape were used to communicate the global impact of Covid-19. While public and private entities offered “big picture” perspectives, hegemonic visualizations often neglected to address the disproportionate toll of the pandemic on the members of marginalized communities. This article presents findings from a mixed-methods investigation of 12 case studies, comparing eight grassroots counter-mapping sources against four mainstream mapping sources, created by government and academic institutions, that will be referred to here as “hegemonic.” The purpose of this study was to investigate how visuals presented online by community-focused counter-mapping collectives differed from those presented by mainstream sources, examining what these differences might indicate about the social imaginaries at play. Case studies from Argentina, Brazil, Canada, and the US produced a corpus of 1,556 images manually collected from online sources. An initial content analysis using NVivo generated quantitative data forming the foundation for later semiotic analysis examining each individual image while also considering the collection holistically. Informed by social semiotics, the findings highlight how counter-mapping employs bespoke illustrations and community insights to portray a more nuanced perspective of the impacts of the pandemic. In contrast, hegemonic maps rely on vector-based graphics that reflect dominant worldviews. Altering the practices of mapping, counter-mapping empowers communities, challenges systemic inequities, and reimagines how visual data shapes public knowledge.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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 it