SUBORDINATED BY THE ALGORITHM: EXPLORING DATA COLONIALISM AMONG LATIN AMERICAN CITIZENS
Bibliographic record
Abstract
Data colonialism refers to the processes by which extracted data is commodified to reproduce and expand capitalist and colonialist practices. As data colonialism transforms infrastructures and ideologies to exercise new ways of control, it has become a crucial approach to better understand how datafication transforms and impacts citizens' lives across the world—especially in the Global South. In this paper, we explore data colonialism as a lens to examine how Latin American citizens' are impacted by their engagement with different information systems. More specifically, we present findings from a collaboration with civic data organizations in five countries in Latin America. Overall, findings show how relying on data colonialism underscores the impacts to citizens when they engage with contemporary information systems, including material and physiological harm to individuals, fragmentation of communities, and various ideological shifts. However, findings also call attention to the value of integrating other theoretical approaches that emphasize discussions about agency, contextualization, and the benefits of datafication. Overall, this paper discusses how data colonialism hurts individuals, target communities, and transforms citizens' imaginaries about their place in society.
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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 | 0.008 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.009 | 0.009 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".