CoChat
Sign Up
Cited by 103Ethics and Social Impacts of AIPoverty, Education, and Child WelfareE-Government and Public Services

Datafying anti-poverty programmes: implications for data justice

Open access
Masiero, Silvia, Das, Soumyo·2019·Information, Communication & Society
DOI 10.1080/1369118x.2019.1575448
Open this paper in CoChat — free accountOpen PDF

Abstract

This paper seeks to illuminate the significance of datafication for anti-poverty programmes, meaning social protection schemes designed specifically for poor people. The conversion of beneficiary populations into machine-readable data enables two core functions of social protection, those of recognising entitled beneficiaries and assigning entitlements connected to each anti-poverty scheme. Drawing on the incorporation of Aadhaar, India’s biometric population database, in the national agenda for social protection, we unpack a techno-rational perspective that crafts datafication as a means to enhance the effectiveness of anti-poverty schemes. Nevertheless, narratives collected in the field show multiple forms of data injustice on recipients, underpinned by Aadhaar’s functionality for a shift of the social protection agenda from in-kind subsidies to cash transfers. Based on such narratives the paper introduces a politically embedded view of data, framing datafication as a transformative force that contributes to reforming existing anti-poverty schemes.

Integrity signals

Registry

Crossref · 10.1080/1369118x.2019.1575448

Cites

36 of 36 references verified0 citation errors · 0 not checkable

Statistics

Recomputing p-values needs the paper’s full text.

Claims the paper makes

We couldn’t retrieve this paper’s full text, so its claims and self-check weren’t run. The registry and citation checks still apply.

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

  • Aadhaar converts individual recipients of programs such as the PDS into machine-readable data.

    “First, as a datafier of the population of Indian residents, Aadhaar converts individual recipients of programs such as the PDS into machine-readable data.”

    From Biometric Infrastructures and the Indian Public Distribution System (2020) · cites this paper as Masiero and Das 2019

  • Aadhaar allows the automatization of two functions of the PDS: recognition of entitled vs. non-entitled users and assignation of entitlements according to data.

    “By its own nature, this process allows the automatization of two functions of the scheme: the recognition of entitled vs. non-entitled users and the assignation of entitlements according to data (Masiero and Das 2019).”

    From Biometric Infrastructures and the Indian Public Distribution System (2020) · cites this paper as Masiero and Das 2019

  • In the latest study of the Aadhaar-based PDS in Karnataka, recipients provided quasi-unanimous negative responses to whether they would welcome a transition from in-kind subsidies to cash transfers.

    “To our question of whether they would welcome a transition from the current in-kind subsidies to cash transfers, recipients provided quasi-unanimous negative responses, motivated by three main reasons.”

    From Biometric Infrastructures and the Indian Public Distribution System (2020) · cites this paper as Masiero and Das 2019

  • The Aadhaar-based PDS confines access to enrolled residents, effectively subordinating the right-to-food to registration in a biometric identification scheme.

    “A first limitation, of a legal nature, shows how the Aadhaar-based PDS—while remaining within the boundaries of constitutionality—confines access to enrolled residents, effectively subordinating the right-to-food to registration in a biometric identification scheme.”

    From Biometric Infrastructures and the Indian Public Distribution System (2020) · cites this paper as Masiero and Das 2019

  • There is incompleteness of information received by PDS beneficiaries on the transformative goal of Aadhaar.

    “In terms of the informational aspect, the limitation lies in the incompleteness of information received by PDS beneficiaries on the transformative goal of Aadhaar.”

    From Biometric Infrastructures and the Indian Public Distribution System (2020) · cites this paper as Masiero and Das 2019

Cites

Flagged first, then by citations

1 of the 37 references couldn’t be matched to a known paper.

Cited by

Sorted by citations

Related

Authors
Masiero, S., & Das, S.
Affiliations
  • Loughborough University (GB)
  • International Institute of Information Technology Bangalore (IN)
Corresponding author
Masiero, Silvia
ORCID
Masiero, Silvia
Venue
Information, Communication & Society
Publisher
Informa UK Limited
Published
2019-05-13
Volume
22
Issue
7
Pages
916-933
ISSN
  • 1369-118X (ISSN-L)
  • 1468-4462
DOI
10.1080/1369118x.2019.1575448
OpenAlex
W2946326213
Cited by
103
References
37
Citations by year
2026: 12 · 2025: 5 · 2024: 11 · 2023: 9 · 2022: 41 · 2021: 12 · 2020: 11 · 2019: 2
FWCI
28.84
Type
journal article
Language
en
Open access
Licence
Locations
  • Information Communication & Society (publishedVersion, cc-by-nc-nd)
  • Loughborough University Institutional Repository (Loughborough University) (submittedVersion, cc-by-nc-nd)
  • Loughborough University Research Repository (Loughborough University) (submittedVersion, cc-by-nc-nd)
Topics
  • Social Sciences › Safety Research › Ethics and Social Impacts of AI
  • Social Sciences › Safety Research › Poverty, Education, and Child Welfare
  • Social Sciences › Political Science and International Relations › E-Government and Public Services
Keywords
datafication, anti-poverty programmes, social protection, data justice, Aadhaar, biometric identification, cash transfers, India

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review