Interrogating data justice on Hyderabad’s urban frontier: information politics and the internal differentiation of vulnerable communities
Bibliographic record
Abstract
How does data visibility affect vulnerable communities that face uncertainty over land tenure? Can data justice be realised in settings of acute resource injustice? These are the overarching questions that our case study interrogates by opening up the black box of the community in the volatile and fast-transforming peri-urban fringe of Hyderabad, India. We examine the unfolding of data and information processes through the lens of enumeration and community mapping exercises conducted in a low-income neighbourhood. We argue that the realisation of data justice is mediated by ‘information politics’, i.e., the ways in which informational resources, as well as the risks and rewards associated with them, are distributed across individual actors and identity groups within the community. The democratising potential of emerging digital technologies is severely constrained by structural inequities across gender, caste, class, and even linguistic lines. Our case study underlines the importance of such a structural understanding of data justice and also suggests directions for embedding justice in data processes. Our findings reveal an arena of stark informational disparities between vulnerable, indigent populations and the increasingly sophisticated digital data apparatuses used to encode them. Efforts to promote data justice must take explicit cognisance of these disparities and fragmentation and recognise the internal structural differentiation of vulnerable communities. We argue for an explicit mapping of the information flows and associated information politics that characterise such settings.
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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 | 0.007 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.020 | 0.051 |
| Scholarly communication | 0.014 | 0.009 |
| Open science | 0.001 | 0.017 |
| Research integrity | 0.002 | 0.003 |
| 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".