Bibliographic record
Abstract
For Indigenous populations in Brazil, maps have long been instruments of invisibility. Official maps have historically misnamed and erased Indigenous territories and communities. At the same time, cartographic representations have been a tool of resistance for Indigenous activists. Indigenous communities and organisations have created their own maps identifying territories, peoples, languages, and cultures. These dynamics of contentious visibility intensified during the Covid-19 pandemic when the spread of the virus among Indigenous populations was poorly reported or even absent from hegemonic contagion maps. State negligence, intensified by an authoritarian government hostile to Indigenous populations, threatened the survival of communities around the country who organised collectively to create their own cartographic representations of the pandemic through resistant appropriations of media and data. This article draws on interviews with Indigenous leaders and media activists to discuss processes of data appropriation and resistant cartographies during the Covid-19 pandemic. Findings highlight the use of data and counter mapping strategies for self-representation and political action that must be understood through a non-media-centric perspective, drawing from conceptualisations at the intersection between human geography, communication, and post-colonial theory.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.012 | 0.015 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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 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".