SUBORDINATED BY THE ALGORITHM: EXPLORING DATA COLONIALISM AMONG LATIN AMERICAN CITIZENS
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
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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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 | 0.006 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.004 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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