Critical data studies with Latin America: Theorizing beyond data colonialism
Bibliographic record
Abstract
The article aims to theorize about critical data studies with Latin America beyond the framework of data colonialism, arguing that the long history of social thought in the region can contribute to a more nuanced understanding of the datafication. It discusses views around dependence, oppressions, and liberation, debating how Latin American authors can be useful for current critical data studies, in a more nuanced and complex vision. It presents the theoretical contributions of Lelia Gonzalez, dependency theorists and Enrique Dussel. Dependency theorists criticize evolutionary frameworks of development and can contribute to discussions around data sovereignty and overexploitation of labor. Gonzalez contributes to a complex vision of Amefrica Ladina, articulating multiple forms of oppression. Enrique Dussel presents a theory of technology considering totality and proposes an ethics of liberation that can be related to alternatives toward data justice and data commons. All theoretical frameworks contribute to thinking about datafication with Latin America not as an isolated phenomenon, but in relation to other countries in the world, and as an analytical key for the construction of alternatives. All perspectives are related to current debates on critical data studies and can make an important contribution to the construction of critical theories about data that consider Latin America also as a site of knowledge production.
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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 | 0.049 | 0.058 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.012 | 0.105 |
| Scholarly communication | 0.022 | 0.029 |
| Open science | 0.002 | 0.016 |
| Research integrity | 0.004 | 0.011 |
| Insufficient payload (model declined to judge) | 0.003 | 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".