Disablism, racism and the spectre of eugenics in digital welfare
Bibliographic record
Abstract
This article explores the historical ties between the digital welfare state and eugenics, highlighting how the use of data infrastructures for classification and governance in the digital era has roots in eugenic data practices and ideas. Through an analysis of three domains of automated decision-making – child welfare, immigration and disability benefits – the article demonstrates how these automated systems perpetuate hierarchical divisions originally shaped by ableist eugenic race science. It underscores the importance of critically engaging with this historical context of data utilisation, emphasising its entanglement with eugenic perspectives on racial, physical and mental superiority, individual and social worth, and the categorisation of data subjects as deserving or undeserving. By engaging with this history, the article provides a deeper understanding of the contemporary digital welfare state, particularly in terms of its discriminatory divisions based on race and disability, which are deeply intertwined.
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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 | 0.015 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.013 | 0.086 |
| Scholarly communication | 0.017 | 0.012 |
| Open science | 0.001 | 0.014 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 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".