Cartographies of Resistance: Counter-Data Mapping as the New Frontier of Digital Media Activism
Bibliographic record
Abstract
In the first datafied pandemic, the production of interactive Covid-19 data maps was intensified by state institutions and corporate media. Maps have been used by states and citizens to understand the advance and retreat of the contagion and monitor vaccine rates. However, the visualisations being used are often based on non-comparable data types across countries, leading to visual misrepresentations. Many pandemic data visualisations have consequently had a negative impact on public debate, contributing to an infodemic of disinformation that has stigmatised marginalised groups and detracted from social justice objectives. Counter to such hegemonic mapping, counter-data maps, produced by marginalised groups, have revealed hidden inequalities, supporting calls for intersectional health justice. This article investigates the ways in which various intersectional global communities have appropriated data, produced counter-data maps, unveiled hidden social realities, and generated more authentic social meanings through emergent counter-data mapping imaginaries. We use a comparative multi-case study, based on a multi case-study of three Covid-19 data mapping projects, namely Data for Black Lives (US), Indigenous Emergency (Brazil), and CityLab maps (global). Our findings indicate that counter-data mapping imaginaries are deeply embedded in community-oriented notions of spatiality and relationality. Moreover, the cartographic process tends to reflect alternative imaginaries through four key dimensions of data mapping practice—objectives, uses, production, and ownership. We argue that counter-data mapping is the new frontier of digital media activism and community communication, as it extends the projects of data justice and community media activism, generating new practices in the activist repertoire of communicative action.
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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 | 0.019 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.010 | 0.009 |
| Science and technology studies | 0.013 | 0.057 |
| Scholarly communication | 0.021 | 0.024 |
| Open science | 0.003 | 0.016 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.009 | 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".