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Record W4409845043 · doi:10.1111/spol.13146

User‐Centred Design in Child Welfare: Importance of an Anti‐Racism Lens

2025· article· en· W4409845043 on OpenAlex
Maria Gintova, Elliot Goodell Ugalde, Abigail Jaimes Zelaya

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.
aboutThe title or abstract carries a Canadian signal from the geographic lexicon.

Bibliographic record

VenueSocial Policy and Administration · 2025
Typearticle
Languageen
FieldComputer Science
TopicInnovative Human-Technology Interaction
Canadian institutionsQueen's UniversityMcMaster University
FundersSocial Sciences and Humanities Research Council of CanadaMcMaster University
KeywordsRacismLens (geology)WelfareThrough-the-lens meteringSociologyHuman–computer interactionPolitical scienceComputer scienceGender studiesOpticsPhysicsLaw

Abstract

fetched live from OpenAlex

ABSTRACT Meaningful engagement of marginalised individuals and communities in formulating government decisions impacting them is beneficial for the whole society. This is especially important for government decisions impacting the child welfare system in Ontario, where Black children and youth are at a higher risk of poor outcomes. However, existing public engagement practices, including user‐centred design initiatives, present significant challenges for Black individuals and communities to influence government decision‐making due to the government's strive for ‘colour‐blind’ solutions. This paper highlights the importance of focusing on Black voices and allies to inform government decision‐making in child welfare and the need for an a priori anti‐racism analysis for user‐centred design initiatives.

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.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.848
Threshold uncertainty score0.376

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.030
GPT teacher head0.328
Teacher spread0.298 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it