Bibliographic record
Abstract
The ‘data revolution’ marks a time of growing interest and investment in data – big, small, or otherwise. Critical attention to data is also proliferating, exposing the diverse ways that data produces inequality of opportunity and harm in society. This paper draws the nascent field of critical data studies into conversation with emerging narratives in data-for-development (D4D) to advance the conceptualization of data inequalities, explaining how they both align with and diverge from core tropes of digital inequalities research – and why this matters for development. The paper examines the causes, consequences, and potential solutions to three ‘data divides’ – access to data, representation of the world as data, and control over data flows – through examples of digital identity systems and national data infrastructures, user-generated data, and personal behavioral data produced through corporate platforms. This understanding provides a basis for future research, practice, and policymaking on data-related (in)equalities in development contexts and beyond.
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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 | 0.017 | 0.058 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.007 | 0.029 |
| Scholarly communication | 0.014 | 0.021 |
| Open science | 0.001 | 0.012 |
| Research integrity | 0.002 | 0.006 |
| Insufficient payload (model declined to judge) | 0.011 | 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".