Bibliographic record
Abstract
This article introduces the tenets of a theory of datafication of and in the Souths. It calls for a de-Westernization of critical data studies, in view of promoting a reparation to the cognitive injustice that fails to recognize non-mainstream ways of knowing the world through data. It situates the “Big Data from the South” research agenda as an epistemological, ontological, and ethical program and outlines five conceptual operations to shape this agenda. First, it suggests moving past the “universalism” associated with our interpretations of datafication. Second, it advocates understanding the South as a composite and plural entity, beyond the geographical connotation (i.e., “global South”). Third, it postulates a critical engagement with the decolonial approach. Fourth, it argues for the need to bring agency to the core of our analyses. Finally, it suggests embracing the imaginaries of datafication emerging from the Souths, foregrounding empowering ways of thinking data from the margins.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.062 | 0.054 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.009 | 0.076 |
| Scholarly communication | 0.020 | 0.046 |
| Open science | 0.002 | 0.025 |
| Research integrity | 0.004 | 0.014 |
| Insufficient payload (model declined to judge) | 0.003 | 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".