Bibliographic record
Abstract
In recent years, critical data studies from the Global South have gained traction, generating debates on power, knowledge production, and the politics of data. While these discussions challenge universalist frameworks, they also risk essentializing the ‘Global South’, requiring a more nuanced approach. This special issue centres Latin America as a site of theoretical, methodological, and empirical inquiry, highlighting its potential to generate new insights into datafication, power, and artificial intelligence. Rather than treating Latin America as a passive recipient of Global North theories, this issue foregrounds its epistemological and methodological contributions to global debates. Engaging with frameworks such as capitalism, coloniality, and dependency theory, the articles explore the region's heterogeneity and intellectual traditions in social sciences, humanities, and science and technology studies. This introduction proposes a research agenda for Latin American critical data studies – one that reflects historical legacies while envisioning possible data futures through interdisciplinary and critical engagement. It interrogates the politics of knowledge production, emphasizing the need for non-extractive, dialogical approaches to studying data in, from, and with Latin America. By centering Latin American scholarship and experiences, this special issue challenges dominant narratives in critical data studies and offers alternative theoretical perspectives that are globally informed yet locally grounded.
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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 | 0.025 | 0.039 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.015 | 0.019 |
| Scholarly communication | 0.016 | 0.009 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.008 | 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".