Cartographies of Negotiation: Data and Pandemic Mapping in the Frena la Curva Initiative
Bibliographic record
Abstract
The Covid-19 pandemic spurred social movements to prioritise solidarity and collective responses. In this context, counter-maps emerged as a form of data activism aimed at illuminating aspects of the crisis often overlooked in dominant representations. This article investigates the Frena la Curva initiative, which used an online forum and a collaborative map to visualise needs and offers of assistance across Ibero-American countries during the pandemic. Drawing on digital ethnography—including website analysis, map examination, and interviews with activists—the study explores the role of mapping in understanding the pandemic and representing diverse human experiences. We argue that maps became central tools for deliberation among data activists, combining practical functions with symbolic significance for social action. To conceptualise this dynamic, we introduce the notion of “cartographies of negotiation,” which highlights the tensions, values, and interpretive practices that shape internet-based maps. This concept foregrounds the distinction between maps as objects and mapping as a situated process. We draw on technocentric and social justice frameworks to examine how activists navigate between technological possibilities and structural constraints, and how their cartographic practices generate new forms of visibility, solidarity, and resistance.
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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 | 0.011 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.006 | 0.010 |
| Science and technology studies | 0.014 | 0.023 |
| Scholarly communication | 0.012 | 0.013 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 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".